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Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.

Limiting the increase of CO2 in the atmosphere is one of the largest challenges of our generation1. Because carbon capture and storage is one of the few viable technologies that can mitigate current CO2 emissions2, much effort is focused on developing solid adsorbents that can efficiently capture CO2 from flue gases emitted from anthropogenic sources3. One class of materials that has attracted considerable interest in this context is metal-organic frameworks (MOFs), in which the careful combination of organic ligands with metal-ion nodes can, in principle, give rise to innumerable structurally and chemically distinct nanoporous MOFs. However, many MOFs that are optimized for the separation of CO2 from nitrogen4-7 do not perform well when using realistic flue gas that contains water, because water competes with CO2 for the same adsorption sites and thereby causes the materials to lose their selectivity. Although flue gases can be dried, this renders the capture process prohibitively expensive8,9. Here we show that data mining of a computational screening library of over 300,000 MOFs can identify different classes of strong CO2-binding sites-which we term 'adsorbaphores'-that endow MOFs with CO2/N2 selectivity that persists in wet flue gases. We subsequently synthesized two water-stable MOFs containing the most hydrophobic adsorbaphore, and found that their carbon-capture performance is not affected by water and outperforms that of some commercial materials. Testing the performance of these MOFs in an industrial setting and consideration of the full capture process-including the targeted CO2 sink, such as geological storage or serving as a carbon source for the chemical industry-will be necessary to identify the optimal separation material.

Biologically derived metal-organic frameworks (bio-MOFs) are of great importance as they can be used as models for bio-mimicking and in catalysis, allowing us to gain insights into how large biological molecules function. Through rational design, here we report the synthesis of a novel bio-MOF featuring unobstructed Watson-Crick faces of adenine (Ade) pointing towards the MOF cavities. We show, through a combined experimental and computational approach, that thymine (Thy) molecules diffuse through the pores of the MOF and become base-paired with Ade. The Ade-Thy pair binding at 40-45% loading reveals that Thy molecules are packed within the channels in a way that fulfill both the Woodward-Hoffmann and Schmidt rules, and upon UV irradiation, Thy molecules dimerize into Thy<>Thy. This study highlights the utility of accessible functional groups within the pores of MOFs, and their ability to 'lock' molecules in specific positions that can be subsequently dimerized upon light irradiation, extending the use of MOFs as nanoreactors for the synthesis of molecules that are otherwise challenging to isolate.

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.

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.

Background: The aim of the present study was to compare the outcomes of pediatric LT with left liver grafts, obtained either from a living donor (LD) or a split deceased donor (sDD). Methods: Retrospective single‐center study from 2002 to 2022. All pediatric LT with left liver grafts (not including middle hepatic vein) from LD or sDD were included. Reduced grafts were not included. Results: A total of 112 pediatric LT were performed: 58 with LD grafts and 54 with sDD grafts (17 split ex situ and 37 in situ). Donor characteristics were similar, apart from donor age (33 years in LD vs. 30 years in sDD, p = 0.03). Indications were similar with 55% biliary atresia in each group. Retransplantation was more frequently performed in the sDD group (2% vs. 15%, p = 0.01). Recipient age, weight, and PELD score at transplant were not significantly different between groups. Cold ischemia time was longer for sDD (158 min in LD vs. 390 min in sDD; p < 0.0001). Posttransplant peak ALT was higher with sDD grafts (1470 vs. 1063, p = 0.018), and hospital stay was longer with sDD grafts (27 vs. 21 days, p = 0.005). However, there was no difference between groups in terms of major morbidity (Dindo‐Clavien grade ≥3), vascular and biliary complications, and 90‐day mortality. Patient survival at 10 years was 93.1% for LD and 92.8% for sDD (p = 0.807). Graft survival at 10 years was 89.7% for LD and 83.1% for sDD (p = 0.813). Conclusions: Technically similar LD and sDD grafts achieve very similar postoperative and long‐term outcomes with excellent patient and graft survival. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) have emerged as versatile materials for applications ranging from gas separation and storage, catalysis, and sensing. The attractive feature of MOFs is that, by changing the ligand and/or metal, they can be chemically tuned to perform optimally for a given application. In most, if not all, of these applications one also needs a material that has a sufficient mechanical stability, but our understanding of how changes in the chemical structure influence mechanical stability is limited. In this work, we rationalize how the mechanical properties of MOFs are related to framework bonding topology and ligand structure. We illustrate that the functional groups on the organic ligands can either enhance the mechanical stability through formation of a secondary network of nonbonded interactions or soften the material by destabilizing the bonded network of a MOF. In addition, we show that synergistic effect of the bonding network of the material and the secondary network is required to achieve optimal mechanical stability of a MOF. The developed molecular insights in this work can be used for systematic improvement of the mechanical stability of the materials by careful selection of the functional groups.

Scientific interest in two-dimensional (2D) materials, ranging from graphene and other single layer materials to atomically thin crystals, is quickly increasing for a large variety of technological applications. While in silico design approaches have made a large impact in the study of 3D crystals, algorithms designed to discover atomically thin 2D materials from their parent 3D materials are by comparison more sparse. We hypothesize that determining how to cut a 3D material in half (i.e., which Miller surface is formed) by severing a minimal number of bonds or a minimal amount of total bond energy per unit area can yield insight into preferred crystal faces. We answer this question by implementing a graph theory technique to mathematically formalize the enumeration of minimum cut surfaces of crystals. While the algorithm is generally applicable to different classes of materials, we focus on zeolitic materials due to their diverse structural topology and because 2D zeolites have promising catalytic and separation performance compared to their 3D counterparts. We report here a simple descriptor based only on structural information that predicts whether a zeolite is likely to be synthesizable in the 2D form and correctly identifies the expressed surface in known layered 2D zeolites. The discovery of this descriptor allows us to highlight other zeolites that may also be synthesized in the 2D form that have not been experimentally realized yet. Finally, our method is general since the mathematical formalism can be applied to find the minimum cut surfaces of other crystallographic materials such as metal-organic frameworks, covalent-organic frameworks, zeolitic-imidazolate frameworks, metal oxides, etc.

We report the syntheses and structures of five metal-organic frameworks (MOFs) based on transition metals (NiII, CuII, and ZnII), adenine, and di-, tri-, and tetra-carboxylate ligands. Adenine, with multiple N donor sites, was found to coordinate to the metal centers in different binding modes including bidentate (through N7 and N9, or N3 and N9) and tridentate (through N3, N7, and N9). Systematic investigations of the protonation states of adenine in each MOF structure via X-ray photoelectron spectroscopy revealed that adenine can be selectively protonated through N1, N3, or N7. The positions of H atoms connected to the N atoms were found from the electron density maps, and further supported by the study of C-N-C bond angles compared to the literature reports. DFT calculations were performed to geometrically optimize and energetically assess the structures simulated with different protonation modes. The present study highlights the rich coordination chemistry of adenine and provides a method for the determination of its protonation states and the location of protonated N atoms of adenine within MOFs, a task that would be challenging in complicated adenine-based MOF structures.

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.

For applications of metal-organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation, we develop a simple analytical model that predicts a material's Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework's intrinsic flexibility whereby performance is either improved or reduced with increasing flexibility, depending on the material's pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that the globally optimal separation exists within an entirely rigid pore. Molecular simulations show that our simple model predicts performance trends that are observed when screening the adsorption behavior of flexible MOFs. These flexible simulations provide better agreement with experimental adsorption data in a high-performance material that is not captured when modeling this framework as rigid, an approximation typically made in high-throughput screening studies. We conclude that, for shape selective adsorption applications, the globally optimal material will have the optimal pore size/chemistry and minimal intrinsic flexibility even though other nonoptimal materials' selectivity can actually be improved by flexibility. Equally important, we find that flexible simulations can be critical for correctly modeling adsorption in these types of systems.

In this work, MOF bulk properties are evaluated and compared using several force fields on several well-studied MOFs, including IRMOF-1 (MOF-5), IRMOF-10, HKUST-1, and UiO-66. It is found that, surprisingly, UFF and DREIDING provide good values for the bulk modulus and linear thermal expansion coefficients for these materials, excluding those that they are not parametrized for. Force fields developed specifically for MOFs including UFF4MOF, BTW-FF, and the DWES force field are also found to provide accurate values for these materials' properties. While we find that each force field offers a moderately good picture of these properties, noticeable deviations can be observed when looking at properties sensitive to framework vibrational modes. This observation is more pronounced upon the introduction of framework charges.

Background: Despite the overall safety and efficacy of COVID‐19 vaccinations, rare cases of systemic autoimmune diseases (SAIDs) have been reported post‐vaccination. This study used a global survey to analyze SAIDs in susceptible individuals' post‐vaccination. Methods: A cross‐sectional study was conducted among participants with self‐reported new‐onset SAIDs using the COVID‐19 Vaccination in Autoimmune Diseases (COVAD) 2 study dataset—a validated, patient‐reported e‐survey—to analyze the long‐term safety of COVID‐19 vaccines. Baseline characteristics of patients with new‐onset SAIDs and vaccinated healthy controls (HCs) were compared after propensity score matching based on age and sex in a 1:4 ratio. Results: Of 16 750 individuals, 74 (median age 52 years, 79.9% females, and 76.7% Caucasians) had new‐onset SAID post‐vaccination, mainly idiopathic inflammatory myopathies (IIMs) (n = 23, 31.51%), arthritis (n = 15; 20.53%), and polymyalgia rheumatica (PMR) (n = 12, 16.40%). Higher odds of new‐onset SAIDs were noted among Caucasians (OR = 5.3; 95% CI = 2.9–9.7; p <.001) and Moderna vaccine recipients (OR = 2.7; 95% CI = 1.3–5.3; p =.004). New‐onset SAIDs were associated with AID multimorbidity (OR = 1.4; 95% CI = 1.1–1.7; p <.001), mental health disorders (OR = 1.6; 95% CI = 1.3–1.9; p <.001), and mixed race (OR = 2.2; 95% CI = 1.2–4.2; p =.010), where those aged >60 years (OR = 0.6; 95% CI = 0.4–0.8; p =.007) and from high/medium human development index (HDI) countries (compared to very high HDI) reported fewer events than HCs. Conclusion: This study reports a low occurrence of new‐onset SAIDs following COVID‐19 vaccination, primarily IIMs, PMR, and inflammatory arthritis. Identified risk factors included pre‐existing AID multimorbidity, mental health diseases, and mixed race. Revaccination was well tolerated by most patients; therefore, we recommend continuing COVID‐19 vaccination in the general population. However, long‐term studies are needed to understand the autoimmune phenomena arising post‐vaccination. [ABSTRACT FROM AUTHOR]