CuI/Fe3O4 NPs@Biimidazole IL‐KCC‐1 as a leach proof nanocatalyst for the synthesis of imidazo[1,2‐a]pyridines in aqueous medium

In the present work, an innovative leach proof nanocatalyst based on dendritic fibrous nanosilica (DFNS) modified with ionic liquid loaded Fe₃O₄ NPs and CuI salts was designed and applied for the rapid synthesis of imidazo[1,2‐a]pyridines from the reaction of phenyl acetylene, 2‐aminopyridine, and aldehydes in aqueous medium. The structure of the synthesized nanocatalyst was studied by field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), Fourier transform infrared (FT‐IR), flame atomic absorption spectroscopy (FAAS), energy‐dispersive X‐ray (EDX), and X‐ray diffraction (XRD), vapor–liquid–solid (VLS), and adsorption/desorption analysis (Brunauer–Emmett–Teller [BET] equation) instrumental techniques. CuI/Fe₃O₄NPs@IL‐KCC‐1 with high surface area (225 m² g^?1) and porous structure not only exhibited excellent catalytic activity in aqueous media but also, with its good stability, simply recovered by an external magnet and recycled for eight cycles without significant loss in its intrinsic activity. Higher catalytic activity of CuI/Fe₃O₄NPs@IL‐KCC‐1 is due to exceptional dendritic fibrous structure of KCC‐1 and the ionic liquid groups that perform as strong anchors to the loaded magnetic nanoparticles (MNPs) and avoid leaching them from the pore of the nanocatalyst. Green reaction media, shorter reaction times, higher yields (71–97%), easy workup, and no need to use the chromatographic column are the advantages of the reported synthetic method.     

Study of triplet kinetic of thermal decomposition reaction and sensitivity to impact and electrostatic discharge of HMX polymorphs

Journal of Thermal Analysis and Calorimetry - The polymorphs of HMX explosive show the different sensitivities to heat, friction, impact, shock, electrostatic charge, etc. Knowledge and...     

Synthesis,characterization, and biological screening of metal complexes of novel sulfonamide derivatives: Experimental and theoretical analysis of sulfonamide crystal

This study reports the synthesis of sulfonamide-derived Schiff bases as ligands L ¹ and L ² as well as their transition metal complexes [VO(IV), Fe(II), Co(II), Ni(II), Cu(II), and Zn(II)]. The Schiff bases (4-{E-[(2-hydroxy-3-methoxyphenyl)methylidene]amino}benzene-1-sulfonamide ( L ¹ ) and 4-{[(2-hydroxy-3-methoxyphenyl)methylidene]amino}-N-(5-methyl-1,2-oxazol-3-yl)benzene-1-sulfonamide ( L ² ) were synthesized by the condensation reaction of 4-aminobenzene-1-sulfonamide and 4-amino-N-(3-methyl-2,3-dihydro-1,2-oxazol-5-yl)benzene-1-sulfonamide with 2-hydroxy-3-methoxybenzaldehyde in an equimolar ratio. Sulfonamide core ligands behaved as bidentate ligands and coordinated with transition metals via nitrogen of azomethine and the oxygen of the hydroxyl group. Ligand L ¹ was recovered in its crystalline form and was analyzed by single-crystal X-ray diffraction technique which held monoclinic crystal system with space group (P2₁/c). The structures of the ligands L ¹ and L ² and their transition metal complexes were established by their physical (melting point, color, yields, solubility, magnetic susceptibility, and conductance measurements), spectral (UV–visible [UV–Vis], Fourier transform infrared spectroscopy, ¹H NMR, ¹³C NMR, and mass analysis), and analytical (CHN analysis) techniques. Furthermore, computational analysis (vibrational bands, frontier molecular orbitals (FMOs), and natural bonding orbitals [NBOs]) were performed for ligands through density functional theory utilizing B3LYP/6-311+G(d,p) level and UV–Vis analysis was carried out by time-dependent density functional theory. Theoretical spectroscopic data were in line with the experimental spectroscopic data. NBO analysis confirmed the extraordinary stability of the ligands in their conjugative interactions. Global reactivity parameters computed from the FMO energies indicated the ligands were bioactive by nature. These procedures ensured the charge transfer phenomenon for the ligands and reasonable relevance was established with experimental results. The synthesized compounds were screened for antimicrobial activities against bacterial (Streptococcus aureus, Bacillus subtilis, Eshcheria coli, and Klebsiella pneomoniae) species and fungal (Aspergillus niger and Aspergillus flavous) strains. A further assay was designed for screening of their antioxidant activities (2,2-diphenyl-1-picrylhydrazine radical scavenging activity, total phenolic contents, and total iron reducing power) and enzyme inhibition properties (amylase, protease, acetylcholinesterase, and butyrylcholinesterase). The substantial results of these activities proved the ligands and their transition metal complexes to be bioactive in their nature.     

Phenylpropanoids from Tanacetum baltistanicum with Nematocidal and Insecticidal Activities

Chemistry of Natural Compounds - One new and seven known secondary metabolites 1–8 were isolated, and the nematocidal and insecticidal activities of major compounds from Tanacetum...     

Quantum Spin Half Algebra and Generalized Megrelishvili Protocol for Confidentiality of Digital Images

International Journal of Theoretical Physics - Most of the enciphering structures are based on the transformation of mediums by describing the strict criteria. The presented article is based on...     

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.     

A deep learning method for estimating the boiling heat transfer coefficient of porous surfaces

Journal of Thermal Analysis and Calorimetry - Owing to the high nucleation site density and relatively robust behavior, sintered coated surfaces are of great interest for thermal management via...     

Determination of the Heat Transfer Coefficient of Metal Oxide Based Water Nanofluids in a Laminar Flow Regime Using an Adaptive Neuro-Fuzzy Inference System

In order to enhance the thermal properties of turbine oil (TO), three different nanoparticles (CuO, Al₂O₃, and TiO₂) are loaded into the TO. To measure the thermal performance of nanoparticle-based TO nanofluids at laminar flow and under constant heat flux boundary conditions, an experimental setup was applied. The obtained data clearly demonstrate the positive effect of all nanoparticles on the heat transfer rate of TO. As the most important factor, the heat transfer coefficient of the abovementioned two-phase systems is increased upon increasing both the volume concentration and the flow rate. An adaptive neuro-fuzzy inference system (ANFIS) is applied for modeling the effect of critical parameters on the heat transfer coefficient of nanoparticle-TO based nanofluids numerically. The results are compared with experimental ones for training and test data. The results suggest that the developed model is valid enough and promising for predicting the extant of the heat transfer coefficient. R² and MSE values for all data were 0.990208751 and 108.1150734, respectively. Based on the results, it is obvious that our proposed modeling by ANFIS is efficient and valid, which can be expanded for more general states.     

Inventory control and pricing for perishable products under age and price dependent stochastic demand

Multistage stochastic programming (SP) with both endogenous and exogenous uncertainties is a novel problem in which some uncertain parameters are decision-dependent and others are independent of decisions. The main difficulty of this problem is that nonanticipativity constraints (NACs) make up a significantly large constraint set, growing very fast with the number of scenarios and leading to an intractable model. Usually, a lot of these constraints are redundant and hence, identification and elimination of redundant NACs can cause a significant reduction in the problem size. Recently, a polynomial time algorithm has been proposed in the literature which is able to identify all redundant NACs in an SP problem with only endogenous uncertainty. In this paper, however, we extend the algorithm proposed in the literature and present a new method which is able to make the upper most possible reduction in the number of NACs in any SP with both exogenous and endogenous uncertain parameters. Proving the validity of this method is another innovation of this study. Computational results confirm that the proposed approach can significantly reduce the problem size within a reasonable computation time.