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In this study, we used a high-throughput computational screening approach to examine the potential of metal–organic frameworks (MOFs) for capturing propane (C3H8) from different gas mixtures. We focused on Quantum MOF (QMOF) database composed of both synthesized and hypothetical MOFs and performed Grand Canonical Monte Carlo (GCMC) simulations to compute C3H8/N2/O2/Ar and C3H8/C2H6/CH4 mixture adsorption properties of MOFs. The separation of C3H8 from air mixture and the simultaneous separation of C3H8 and C2H6 from CH4 were studied for six different adsorption-based processes at various temperatures and pressures, including vacuum-swing adsorption (VSA), pressure-swing adsorption (PSA), vacuum–temperature swing adsorption (VTSA), and pressure-temperature swing adsorption (PTSA). The results of molecular simulations were used to evaluate the MOF adsorbents and the type of separation processes based on selectivity, working capacity, adsorbent performance score, and regenerability. Our results showed that VTSA is the most effective process since many MOFs offer high regenerability (>90%) combined with high C3H8 selectivity (>7 × 103) and high C2H6 + C3H8 selectivity (>100) for C3H8 capture from air and natural gas mixtures, respectively. Analysis of the top MOFs revealed that materials with narrow pores (<10 Å) and low porosities (<0.7), having aromatic ring linkers, alumina or zinc metal nodes, typically exhibit a superior C3H8 separation performance. The top MOFs were shown to outperform commercial zeolite, MFI for C3H8 capture from air, and several well-known MOFs for C3H8 capture from natural gas stream. These results will direct the experimental efforts to the most efficient C3H8 capture processes by providing key molecular insights into selecting the most useful adsorbents. [ABSTRACT FROM AUTHOR]

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 study, it is aimed to provide alternative fibres to cotton with enhanced comfort level, environmentally friendly and colour‐enriched in the cellulosic knitted fabric field. For this purpose, in addition to 100% cotton, 100% linen, 100% hemp yarns, knitted fabrics were produced from 70% cotton/30% linen and 70% cotton/30% hemp yarns. First of all, the properties of yarns such as tenacity, elongation at break, yarn unevenness, thin places, thick places and neps were examined comparatively. Then, pique fabrics were produced from these yarns and dyed with a reactive dye to a selected colour. Afterwards, physical (weight, wale/course density), mechanical (bursting strength, pilling, abrasion resistance) and comfort (air permeability and water vapour permeability) properties of all fabric samples, both in raw form and after dyeing and finishing processes, were compared. Furthermore, dyeing properties (colour, dye‐uptake, dyeing levelness, fastness) of fabric samples were also investigated. The dye uptake (%) values of the yarns decrease in the order of cotton > cotton/hemp > cotton/linen > hemp > linen. However, fastness values of dyed fabrics were nearly identical. Physical and mechanical properties of fabrics were very similar, while the air permeability of the fabrics decrease in the order of hemp > linen > cotton/hemp > cotton/linen > cotton. As a result of the study, it has been possible to produce knitted fabrics with superior performance characteristics (dyeability, comfort, etc.) from yarns produced via blending natural cellulosic fibres (flax and hemp) with certain proportions of cotton fibres, which contribute to sustainable production. [ABSTRACT FROM AUTHOR]

Membrane‐based separation can offer significant energy savings over conventional separation methods. Given their highly customizable and porous structures, metal–organic frameworks‐ (MOFs) are considered as next‐generation membrane materials that can bring about high separation performance and energy efficiency in various separation applications. Yet, the enormously large number of possible MOF structures necessitates the development and implementation of efficient modeling approaches to expedite the design, discovery, and selection of optimal MOF‐based membranes via directing the experimental efforts, time, and resources to the potentially useful membrane materials. With the recent developments in the field of atomic simulations and artificial intelligence methods, a new era of membrane modeling has started. This review focuses on the recent advances made and key strategies used in the modeling of MOF‐based membranes and highlight the huge potential of combining atomistic modeling of MOFs with machine learning to explore very large number of MOF membranes and MOF/polymer composite membranes for gas separation. Opportunities and challenges related to the implementation of data‐driven approaches to extract useful structure–property relations of MOF‐based membranes and to produce design principles for the high‐performing MOF‐based membranes are discussed. [ABSTRACT FROM AUTHOR]

This study aimed to examine the impact of SCD Probiotics supplementation on liver biomolecule content and histological changes during a 30‐day intermittent fasting (IF) program in 24‐month‐old male Sprague–Dawley rats. Rats underwent 18‐h daily fasting and received 1 × 108 CFU of SCD Probiotics daily. Liver tissue biomolecules were analysed using FTIR Spectroscopy, LDA, and SVM techniques, while histopathological evaluations used Haematoxylin and eosin and Masson trichrome‐stained tissues. Blood samples were collected for biochemical analysis. Gross alterations in the quantity of biomolecules were observed with individual or combined treatments. LDA and SVM analyses demonstrated a high accuracy in differentiating control and treated groups. The combination treatments led to the most significant reduction in cholesterol ester (1740 cm−1) and improved protein phosphorylation (A1239/A2955 and A1080/A1545) and carbonylation (A1740/A1545). Individually, IF and SCD Probiotics were more effective in enhancing membrane dynamics (Bw2922/Bw2955). In treated groups, histological evaluations showed decreased hepatocyte degeneration, lymphocyticinfiltration, steatosis and fibrosis. Serum ALP, LDH and albumin levels significantly increased in the SCD Probiotics and combined treatment groups. This study offers valuable insights into the potential mechanisms behind the beneficial effects of IF and SCD Probiotics on liver biomolecule content, contributing to the development of personalized nutrition and health strategies. [ABSTRACT FROM AUTHOR]

Objective: Intellectual disability (ID) is a complex, variable, and clinically heterogeneous neurodevelopmental disorder that affects 1% - 3% of the global population. Copy number variations (CNVs) contribute to approximately 15%-20% of ID cases. Array comparative genomic hybridization (aCGH) is the first-line test for diagnosing patients with ID with/without multiple congenital anomalies (MCAs). This study aimed to present CNVs identified in a retrospective aCGH cohort of Turkish patients with ID with/without other medical conditions. Materials and Methods: The study population consisted of 210 patients (139 male, 71 female) aged 2-18 years. aCGH analysis was performed using oligo and bacterial artificial chromosome (BAC)-based microarray platforms. CNVs were interpreted using public databases and literature mining and categorized according to international guidelines. Results: Forty-five CNVs were detected in 38 (18%) patients. Among these CNVs, 21 (46.6%) were pathogenic, 4 (8.8%) were likely pathogenic, and 8 (17.7%) were variants of uncertain clinical significance (VUS). Nineteen CNVs corresponded to rare microdeletion/ microduplication syndromes. Conclusions: This study reports rare CNVs or syndromes among Turkish patients with ID with/without other medical conditions. Data revealed an overall diagnostic rate of 11.43%, which confirms aCGH as the first-line technology allowing geneticists to diagnose complex phenotypes, identify candidate genes involved in ID, and explore novel CNV effects. [ABSTRACT FROM AUTHOR]