Improving the Mechanical Stability of Metal-Organic Frameworks Using Chemical Caryatids

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

Anomalous Effects of Velocity Rescaling Algorithms: The Flying Ice Cube Effect Revisited

The flying ice cube effect is a molecular dynamics simulation artifact in which the use of velocity rescaling thermostats sometimes causes violation of the equipartition theorem, affecting both structural and dynamic properties. The reason for this artifact and the conditions under which it occurs have not been fully understood. Since the flying ice cube effect was first demonstrated, a new velocity rescaling algorithm (the CSVR thermostat) has been developed and become popular without its effects on the equipartition theorem being truly known. Meanwhile, the use of simple velocity rescaling and Berendsen (weak coupling) thermostat algorithms has not abated but has actually continued to grow. Here, we have calculated the partitioning of the kinetic energy between translational, rotational, and vibrational modes in simulations of diatomic molecules to explicitly determine whether the equipartition theorem is violated under different thermostats and while rescaling velocities to different kinetic energy distributions. We have found that the underlying cause of the flying ice cube effect is a violation of balance leading to systematic redistributions of kinetic energy under simple velocity rescaling and the Berendsen thermostat. When velocities are instead rescaled to the canonical ensemble's kinetic energy distribution, as is done with the CSVR thermostat, the equipartition theorem is not violated, and we show that the CSVR thermostat satisfies detailed balance. The critical necessity for molecular dynamics practitioners to abandon the use of popular yet incorrect velocity rescaling algorithms is underscored with an example demonstrating that the main result of a highly cited study is entirely due to artifacts resulting from the study's use of the Berendsen thermostat

Effects of Modafinil on Sleep Pattern during Methamphetamine Withdrawal: A Double-blind Randomized Controlled Trial

Background: Methamphetamine (MA) abuse is a serious and costly public health problem worldwide; It also commonly affects the sleep quality. The present study was carried out aiming to evaluate the effectiveness of modafinil versus placebo on sleep pattern in MA withdrawal during an eight-week period. Methods: In a double-blind randomized controlled study, a total of 80 patients with a confirmed diagnosis MA withdrawal were treated with modafinil (200 mg/day). Pittsburgh Sleep Quality Index (PSQI) and Epworth sleepiness scale (ESS) were used to assess sleep pattern in the 1th and 56th days of the study. Analysis of covariance (ANCOVA) was applied to compare the groups. All analyses were performed by using SPSS software with a 5% significance level. Findings: The mean age of the people in the intervention and placebo groups was 32.92 ± 2.06 and 34.08 ± 2.13 years, respectively. The mean scores of ESS decreased from 16.15 ± 4.50 to 9.15 ± 3.34 after the intervention in the modafinil group (P < 0.001), with no significant reduction in the placebo group (P = 0.990). The mean scores of PSQI decreased from 13.88 ± 3.40 to 9.92 ± 3.10 after the intervention in the modafinil group (P < 0.001), however there was no significant reduction in the placebo group (P = 0.980). The value of the Eta effect size of the PSQI and ESS questionnaires was 0.52 and 0.72, respectively. Modafinil was superior to placebo in improving the PSQI and ESS scales in the 56th day of assessment (P < 0.050). Conclusion: Modafinil improves the sleep quality in patients with MA withdrawal

Organizational Climate of the COVID-19 Intensive Care Units: A Qualitative Content Analysis Study

Introduction: To manage the psychological consequences of providing services in the COVID-19 intensive care units (ICUs), it is necessary to identify the experience of nurses from the organizational climate. The current study was conducted to explain the nurses’ experience of the organizational climate of the COVID-19 ICUs. Methods: This qualitative study was conducted in three teaching hospitals affiliated to Isfahan University of Medical Sciences. 17 individual and semi-structured interviews with 12 nurses working in three selected COVID-19 centers were included in the data analysis. The participants were selected by purposive sampling and interviewed in one or more sessions at a suitable time and place. Interviews lasted for 45 to 90 minutes and continued with conventional content analysis until data saturation. Data analysis was done using conventional content analysis of Graham and Leideman model. Guba and Lincoln criteria (including validity, transferability, consistency, and reliability) were used to ensure reliability and accuracy. Results: The results of data analysis were classified into 82 primary concept codes and 10 sub-categories in the form of 3 categories: "positive climate of attachment and professional commitment", "emotional resonance in the work environment" and "supportive environment of the organization". Conclusion: This study led to the identification of nurses’ experiences of the organizational climate during the COVID-19 which provides appropriate information to nursing managers to create a favorable organizational climate and increase the quality of work-life of nurses

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

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining

The vastness of materials space, particularly that which is concerned with metal–organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties

High-Throughput Screening Approach for Nanoporous Materials Genome Using Topological Data Analysis: Application to Zeolites

The materials genome initiative has led to the creation of a large (over a million) database of different classes of nanoporous materials. As the number of hypothetical materials that can, in principle, be experimentally synthesized is infinite, a bottleneck in the use of these databases for the discovery of novel materials is the lack of efficient computational tools to analyze them. Current approaches use brute-force molecular simulations to generate thermodynamic data needed to predict the performance of these materials in different applications, but this approach is limited to the analysis of tens of thousands of structures due to computational intractability. As such, it is conceivable and even likely that the best nanoporous materials for any given application have yet to be discovered both experimentally and theoretically. In this article, we seek a computational approach to tackle this issue by transitioning away from brute-force characterization to high-throughput screening methods based on big-data analysis, using the zeolite database as an example. For identifying and comparing zeolites, we used a topological data analysis-based descriptor (TD) recognizing pore shapes. For methane storage and carbon capture applications, our analyses seeking pairs of highly similar zeolites discovered good correlations between performance properties of a seed zeolite and the corresponding pair, which demonstrates the capability of TD to predict performance properties. It was also shown that when some top zeolites are known, TD can be used to detect other high-performing materials as their neighbors with high probability. Finally, we performed high-throughput screening of zeolites based on TD. For methane storage (or carbon capture) applications, the promising sets from our screenings contained high-percentages of top-performing zeolites: 45% (or 23%) of the top 1% zeolites in the entire set. This result shows that our screening approach using TD is highly efficient in finding high-performing materials. We expect that this approach could easily be extended to other applications by simply adjusting one parameter, the size of the target gas molecule

Rapid, selective heavy metal removal from water by a metal-organic framework/polydopamine composite

Drinking water contamination with heavy metals, particularly lead, is a persistent problem worldwide with grave public health consequences. Existing purification methods often cannot address this problem quickly and economically. Here we report a cheap, water stable metal–organic framework/polymer composite, Fe-BTC/PDA, that exhibits rapid, selective removal of large quantities of heavy metals, such as Pb2+ and Hg2+, from real world water samples. In this work, Fe-BTC is treated with dopamine, which undergoes a spontaneous polymerization to polydopamine (PDA) within its pores via the Fe3+ open metal sites. The PDA, pinned on the internal MOF surface, gains extrinsic porosity, resulting in a composite that binds up to 1634 mg of Hg2+ and 394 mg of Pb2+ per gram of composite and removes more than 99.8% of these ions from a 1 ppm solution, yielding drinkable levels in seconds. Further, the composite properties are well-maintained in river and seawater samples spiked with only trace amounts of lead, illustrating unprecedented selectivity. Remarkably, no significant uptake of competing metal ions is observed even when interferents, such as Na+, are present at concentrations up to 14 000 times that of Pb2+. The material is further shown to be resistant to fouling when tested in high concentrations of common organic interferents, like humic acid, and is fully regenerable over many cycles