Sayyed Hossein Hashemi; Massoud Kaykhaii; Mohamad Shakeri
Abstract
In this research, a new modeling method based on three-layer artificial neural network (ANN) technique was applied to predict the extraction yield of copper-morin complex from aqueous samples by means of molecularly imprinted stir bar sorptive extraction. Input variables of the model were pH of the solution, ...
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In this research, a new modeling method based on three-layer artificial neural network (ANN) technique was applied to predict the extraction yield of copper-morin complex from aqueous samples by means of molecularly imprinted stir bar sorptive extraction. Input variables of the model were pH of the solution, absorption and desorption times, stirring rate, temperature, and amount of morin ligand; while the output was extraction yield of copper ions. It was found that a network with 12 hidden neurons is highly accurate in predicting extraction recovery of copper-morin complex. The mean squared error and correlation coefficient between the experimental data and the ANN predictions were achieved as 0.0009 and 0.9999 for training, 0.0032 and 0.976 for validation and 0.0030 and 0.96666 for testing data sets. Under the optimum conditions, the linear range found to be in the range of 5-1000 μg L-1 with the detection limit of 0.38 μg L-1. The relative standard deviation was obtained to be below 5.3%. The method was successfully applied for preconcentration and determination of Cu in a few real samples.
Mohammad Reza Rezaei Kahkha; Massoud Kaykhaii; Mahdi Shafee-Afarani; Batool Rezaei Kahkha
Volume 4, Issue 2 , September 2017, , Pages 10-16
Abstract
In this work, a microextraction technique based on pipette tip solid-phase extraction was used for preconcentration and determination of diazinon. Carbon nanotube functionalized by zinc sulfide and ethylene glycol was used as sorbent. Determination of diazinon was performed using high performance liquid ...
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In this work, a microextraction technique based on pipette tip solid-phase extraction was used for preconcentration and determination of diazinon. Carbon nanotube functionalized by zinc sulfide and ethylene glycol was used as sorbent. Determination of diazinon was performed using high performance liquid chromatography and UV detection. Important parameters that influence the extraction efficiency (i.e. pH, amount of adsorbent, extraction time, salt addition, volumes of sample and eluting solvent and number of aspirating/dispensing cycles for both solvent and sample) were investigated and optimized. Results were showed that method was validated over the range of 0.50 - 100.0 µg L-1. Repeatability was satisfactory, bellow 3.78% for 5 replicate measurements of 20 µg L-1 of diazinon. The limit of detection of this method is 0.03 µg L-1 with an enrichment factor of 100 and short extraction time of 8.5 min, which confirmed suggested method is a reliable and accurate for extraction and preconcentration of diazinon.