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This catalytic method is effective for the simultaneous activation of the carbonyl group and N–H bond by using the Al 2O 3 catalyst. The products were purified by recrystallization and column chromatography techniques. The catalyst was reused several times with no significant loss in its catalytic activity. In this study, five successful compounds were synthesized by the transamidation of secondary amides with amines using a reusable Al 2O 3 catalyst.
Al2o3 gaussian software software#
Moreover, using the Gaussian09 software at the DFT level, HUMO, LUMO and the intrinsic reaction coordinates (IRCs) have also been calculated to find out the transition state of the reaction and energy. Several aliphatic and aromatic amines were used for the transamidation of N-methylbenzamide in the presence of the Al 2O 3 catalyst. By using the concepts of amphoteric properties of Al 2O 3, amides were synthesized from secondary amides and amines in the presence of triethylamine solvent. Al 2O 3 is an amphoteric catalyst that activates the carbonyl carbon of the secondary amide group and helps the C–N cleavage of the reactant amide group by attacking the N–H hydrogen. Unfortunately, the traditional synthesis of amides suffers from some important drawbacks, including low atom efficiency, high catalyst loading, separation of products from the reaction mixture and production of byproducts. Regression coefficient value (R²) is 0.99 nearer to one hence the predicted results are reliable.Amides are the most extensively used substances in both synthetic organic and bioorganic chemistry. It is observed that an optimized Gaussian process regression (GPR) method with matern kernel function shows an accurate agreement with experimental data with Root Mean Square Error (RMSE) value of 0.000126 for TCR and squared exponential kernel function show good agreement with experimental data with Root Mean Square Error (RMSE) value of 0.000045 for DVR. The predictions were evaluated by various evaluation criterions. The proposed modeling is performed by using MATLAB software. 222 experimental data sets are taken to predict the thermal conductivity ratio (TCR), dynamic viscosity ratio (DVR) and also the effectiveness of the predictor variables in predicting the response variables are extensively studied and found that the temperature is the crucial factor to enhance the thermal conductivity ratio.
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The input predictor variables used in this model are temperature, volume fraction and size of the nanoparticles. In this research paper, thermal conductivity ratio and dynamic viscosity ratio of Al₂O₃/H₂O nanofluid are predicted accurately by using Gaussian Process Regression (GPR) methods. Moreover, numerous experimental tests are required to acquire the thermal conductivity of nanofluids accurately. In real time situation it is challenging to determine the thermal conductivity of nanofluids with accuracy as they have many depending factors. Many investigators suggested that the nanofluids have the potential to apply in various engineering fields. e03966 ISSN: 2405-8440 Subject: computer software, models, nanofluids, normal distribution, regression analysis, temperature, thermal conductivity, viscosity Abstract: Nanofluids possess higher thermal properties than the other conventional base fluids. Kavitha Source: Heliyon 2020 v.6 no.6 pp. Regression analysis for thermal properties of Al2O3/H2O nanofluid using machine learning techniques Author: P.C.