An Efficient Synthesis of Benzimidazole and Benzothiazole Derivatives Using a Nickel(II) Metal-Organic Framework.

BACKGROUND: The benzimidazoles and benzothiazoles have shown relatively high pharmaceutical and biological activities. In recent years, numerous methods have been developed for synthesis of benzimidazole and benzothiazole derivatives using different catalysts. However, only some of the reported procedures are quite satisfactory and most of them have drawbacks. Herein, we report a convenient method for synthesis of benzimidazole and benzothiazole derivatives using a nickel (II) metal-organic framework (Ni- MOF) as a novel and reusable catalyst. The presence of unsaturated metal centers makes metal-organic frameworks to be used as Lewis acid catalysts. OBJECTIVE: The primary objective of this study was to describe an efficient method for synthesis of benzimidazole and benzothiazole derivatives. METHOD: Ni-MOF was prepared using the modified evaporation method and was characterized by FE-SEM, FT-IR, TGA, and XRD techniques.The catalyst was then used to test the synthesis of some benzimidazole and benzothiazole derivatives. The benzimidazoles and benzothiazoles were characterized by Elemental analyses, HNMR and IR techniques. RESULT: A variety of aromatic aldehydes bearing electron donating groups or electron-withdrawing were reacted with 1,2-phenylenediamine or 2-aminothiophenol using Ni-MOF in good to excellent yields. CONCLUSION: In summary, a new and highly efficient method was developed and reported for the synthesis of benzimidazole and benzothiazole derivatives using nickel(II) metal-organic framework. The advantages are short reaction times, good to excellent yields, the environmentally benign and simple procedure, stability, nontoxicity, recyclability, and easy separation of the catalyst.

Decolorization of crystal violet from aqueous solutions by a novel adsorbent chitosan/nanodiopside using response surface methodology and artificial neural network-genetic algorithm.

A novel adsorbent of chitosan/nanodiopside nanocomposite (CS-NDIO) was synthesized as a green composite for the removal of crystal violet (CV) and characterized by techniques like XRD, FT-IR, BET, and FESEM analysis. The influence of parameters like molar ratios of CS to NDIO, initial pH of the solution, dosage of adsorbent, initial concentration of CV and contact time was investigated and evaluated by central composite design (CCD; 5 levels and 4 factors). Also, Hybrid model of (ANN) model with genetic algorithm (GA) optimization was applied to the experimental data get through CCD. The optimized molar ratio of CS-NDIO was found: 20/80. Optimal parameter choice for maximum CV adsorption process using CCD and ANN-GA were as follows: pH = 7.50 and 7.499, adsorbent mass: 0.0077 and 0.0077 g, CV concentration: 20.000 and 20.002 mg/L, and contact time: 25.00 and 25.00 min, respectively. The evaluation adsorption equilibrium and kinetic data were fitted with the Langmuir monolayer isotherm model (qmax: 104.66 mg g-1 and R2: 0.9937) and pseudo-second order kinetics mechanism (R2: 0.9978). Thermodynamic parameters (R2: 0.9180, ΔH°: -74.93 kJ mol-1, ΔG°: -12.89 kJ mol-1, and ΔS°: 0.93 kJ mol-1 K-1) were calculated and indicating adsorption to be an exothermic and spontaneous process.

Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives.

AIM AND OBJECTIVE: Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors. MATERIALS AND METHODS: Thermodynamic properties of camptothecin such as the thermal energy, entropy and heat capacity were calculated at Hartree-Fock level of theory and 3-21G basis sets by Gaussian 09. RESULTS: The appropriate descriptors for the studied properties are computed and optimized by the genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate predictive models by partitioning the total sample into training and test sets. CONCLUSION: The predictive ability of the models was found to be satisfactory and could be used for predicting thermodynamic properties of camptothecin derivatives.