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Eslam Pourbasheer,Somayeh Morsali,Alireza Banaei,Sajjad Aghabalazadeh,Mohammad Reza Ganjali,Parviz Norouzi 한국공업화학회 2015 Journal of Industrial and Engineering Chemistry Vol.26 No.-
The characterization of an optode is described for detection of copper(II) based on the immobilization of 6-bromo-3-(2-methyl-2,3-dihydrobenzo[d]thiazol-2-yl)-2H-chromen-2 one on a triacetylcellulose membrane. The effects of pH, indicator concentration and reaction time on the immobilization of ligand were studied. This optode can readily be regenerated using thiourea solutions and its response was reproducible and reversible (R.S.D. less than 2.7%). The Cu(II) could be determined in the range between 7.0 107 and 1.0 104 M with a detection limit of 2.5 107 M. The optical sensor was successfully applied for the determination of copper(II) in various real samples.
Eslam Pourbasheer,Alireza Banaei,Reza Aalizadeh,Mohammad Reza Ganjali,Parviz Norouzi,Javad Shadmanesh,Constantinos Methenitis 한국공업화학회 2015 Journal of Industrial and Engineering Chemistry Vol.21 No.1
Quantitative structure property relationship study of Fullerene derivatives was studied to predict thepower conversion efficiency of compounds as polymer solar cell acceptors. The data set was split into thetraining and test set by employing hierarchal cluster technique. The most relevant descriptors wereselected using the genetic algorithm (GA) method. The predictive ability of the constructed model wasevaluated using Y-randomization test, cross-validation and test set compounds. The GA–MLR model wasbuilt based on six molecular descriptors, and it revealed appropriate statistical results. The resultssuggested that some quantum-chemical descriptors play significant effects on increasing the PCE values.
Habibi-Yangjeh, Aziz,Pourbasheer, Eslam,Danandeh-Jenagharad, Mohammad Korean Chemical Society 2008 Bulletin of the Korean Chemical Society Vol.29 No.4
Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of theoretical descriptors were calculated for each compound. The first 234 principal components (PC’s) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PC’s, the genetic algorithm was employed for selection of the best set of extracted PC’s for PC-MLR and PC-ANN models. The models were generated using fifteen PC’s as variables. For evaluation of the predictive power of the models, melting points of 64 compounds in the prediction set were calculated. Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and $12.77{^{\circ}C}$, respectively. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE = $40.7{^{\circ}C}$). The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.
Aziz Habibi-Yangjeh*,Eslam Pourbasheer,Mohammad Danandeh-Jenagharad 대한화학회 2008 Bulletin of the Korean Chemical Society Vol.29 No.4
Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of theoretical descriptors were calculated for each compound. The first 234 principal components (PCs) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PCs, the genetic algorithm was employed for selection of the best set of extracted PCs for PC-MLR and PC-ANN models. The models were generated using fifteen PCs as variables. For evaluation of the predictive power of the models, melting points of 64 compounds in the prediction set were calculated. Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and 12.77 C, respectively. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE = 40.7 C). The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.