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In this work, 2-thioxo -3 N-(2-ethoxyphenyl -5[4′-methyl -3′N-(2′-ethoxyphenyl) thiazol-2′(3′H)-ylidene] thiazolidin-4-one (E2Th2) was synthesized and its molecular structure determined by FT-IR, UV-Vis and NMR spectroscopic techniques along with a single crystal X-ray diffraction study. Quantum chemical calculations were performed to better understand its structural, spectroscopic and electronic properties such as HOMO and LUMO energy gap, MEP and global reactivity descriptors. Theoretical calculations were performed using density functional theory (DFT) based on the B3LYP/6-311G (d,p). Vibrational frequencies were assigned on the basis of PED and electronic transitions are calculated according to time-dependent density functional theory (TD-DFT). The HOMO and LUMO analysis are used to determine the charge transfer within the molecule. The NLO activity evaluation was based on the determination of the dipole moment, polarizability (α), first-order hyperpolarizability (β) and the second-order hyperpolarizability (γ) which were calculated using the same level of theory. The hyper-hardness descriptor (Γ) was explored and correlated with the NLO properties. Additionally, the stability of the molecule occurring from hyper-conjugative interaction, intramolecular hydrogen bond and charge delocalization were quantified using NBO analysis. The repulsive, attractive, and van der Waals strong and weak interactions were revealed by RDG and HS analyses. Finally, results have shown that the title compound can be used as attractive future NLO material. [ABSTRACT FROM AUTHOR]

AbstractA novel heterocyclic compound named (Z)-5-(4-nitrobenzylidene)-3-N(3-chlorophenyl)-2-thioxothiazolidin-4-one (NCTh) was synthesized and analyzed using various techniques, including single crystal X-ray diffraction, FT-IR, UV-Vis, NMR spectroscopy, and mass spectral data methods. NCTh was observed to crystallize in the triclinic crystal system P-1 space group. To validate the experimental findings, density functional theory (DFT) calculations were performed using the B3LYP functional with the 6-311 G(d,p) basis set. The results indicated good agreement in geometrical parameters between the experimental and theoretical outcomes. The study also calculated HOMO-LUMO energies, molecular electrostatic potential (MEP), and global chemical reactivity descriptors to evaluate the compound’s reactivity. Additionally, the study conducted reduced density gradient and Hirshfeld surface analyses to reveal attractive, repulsive, and van der Waals strong and weak interactions. The calculation of thermodynamic parameters was also included. The study focused on investigating the nonlinear optical (NLO) properties of NCTh, which were characterized through key parameters such as the electric dipole moment (µ), polarizability (α), first-order hyperpolarizability (β), and second-order hyperpolarizability (γ). These properties provide insights into the material’s potential applications in optical and photonic technologies. Additionally, a molecular docking study was performed to further explore the structure-activity relationship, particularly in a biological context. The docking results revealed a strong ligand-protein interaction, with a binding energy of −10.3 kcal/mol, indicating significant affinity. Moreover, the inhibition constant (Ki) was calculated to be 0.0281 μM, suggesting a highly potent interaction, which could be relevant for drug design or biochemical applications. [ABSTRACT FROM AUTHOR]

Accurate prediction of the electrical conductivity of ionic liquids (ILs) is crucial for their efficient design and application in various fields. In this study, new group contribution (GC) method is proposed for the prediction of the electrical conductivity of pure ILs as a function of temperature (T) and pressure (p). This group contribution scheme enables the comprehensive description of diverse ionic liquids and the differentiation among isomers. Four machine learning algorithms, namely Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machines (SVM) and Deep Neural Networks (DNN) are developed and compared with a conventional approach and other methods recently proposed in the literature. The proposed models are developed using the largest collection of data reported to date, which includes a total of 7598 data points for 520 ionic liquids of various chemical families. This database underwent a rigorous and thorough review, evaluation, and reduction process to ensure its reliability. The GC scheme was adopted from our previous work. The results indicate that the DNN model outperforms other approaches, closely followed by the SVM model. The predictive accuracy of both models falls within the experimental uncertainty range, validating their reliability. Additionally, the influence of cationic and anionic families on model performance is explored, providing valuable insights into IL behavior. [Display omitted] [ABSTRACT FROM AUTHOR]