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| Abstract: | The electron donating properties of La2O3 activated at 300, 500 and 800·C and its mixed oxides with alumina are reported from the studies on adsorption of electron acceptors of varying electron affinity on La203. The electron acceptors with their electron affinity values given in parenthesis are: 7,7,8,8-tetracyanoquinodimethane (2.84 eV), 2,3,5,6-tetrachloro-I,4-benzoquinone (2.40 eV) and p-dinitrobenzene(l.77eV). The basicity of the oxide has been determined by titration with n-butylamine and Ho.max values are reported. The limit of electron transfer from the oxide to the electron acceptor is between 2.40 and 1.77 eV. It is observed that La203 promotes the surface electron properties of alumina without changing its limit of electron transfer. |
| URI: | http://dyuthi.cusat.ac.in/purl/2217 |
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| Dyuthi-SS22.pdf | (236.1Kb) |
| Abstract: | Lanthanum oxide, La2O3 has been found to be an effective catalyst for the liquid phase reduction of cyclohexanone. The catalytic activities of La2O3 activated at 300, 500 and 800·C and its mixed oxides with alumina for the reduction of cyclohexanone with 2-propanol have been determined and the data parallel that of the electron donating properties of the catalysts. The electron donating properties of the catalysts have been determined from the adsorption of electron acceptors of different electron affinities on the surface of these oxides. |
| URI: | http://dyuthi.cusat.ac.in/purl/2220 |
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| Dyuthi-SS24.pdf | (275.1Kb) |
| URI: | http://dyuthi.cusat.ac.in/purl/1731 |
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| Dyuthi-T0126.pdf | (2.482Mb) |
| Abstract: | The limit of electron transfer in electron affinity from the oxide surface to the electron acceptor (EA) are reported from the adsorption of EA on DY203, mixed oxides of DY203 with alumina and mixed oxides of Y203 with y-alumina. The extent of electron transfer is understood from magnetic measurements. |
| URI: | http://dyuthi.cusat.ac.in/purl/2275 |
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| Dyuthi-SS55.pdf | (210.7Kb) |
| Abstract: | In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576 |
| Description: | PROCEEDINGS OF ICETECT 2011 |
| URI: | http://dyuthi.cusat.ac.in/purl/4235 |
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| Using Neural Ne ... Drug – like compounds.pdf | (1.156Mb) |
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