| Literature DB >> 27544797 |
Fatemeh Mehrabi1, Azam Vafaei2, Mehrorang Ghaedi3, Abdol Mohammad Ghaedi1, Ebrahim Alipanahpour Dil4, Arash Asfaram4.
Abstract
In this research, a selective, simple and rapid ultrasound assisted dispersive solid-phase micro-microextraction (UA-DSPME) was developed using cobalt ferrite nanoparticles loaded on activated carbon (CoFe2O4-NPs-AC) as an efficient sorbent for the preconcentration and determination of Maxilon Red GRL (MR-GRL) dye. The properties of sorbent are characterized by X-ray diffraction (XRD), Transmission Electron Microscopy (TEM), Vibrating sample magnetometers (VSM), Fourier transform infrared spectroscopy (FTIR), Particle size distribution (PSD) and Scanning Electron Microscope (SEM) techniques. The factors affecting on the determination of MR-GRL dye were investigated and optimized by central composite design (CCD) and artificial neural networks based on genetic algorithm (ANN-GA). CCD and ANN-GA were used for optimization. Using ANN-GA, optimum conditions were set at 6.70, 1.2mg, 5.5min and 174μL for pH, sorbent amount, sonication time and volume of eluent, respectively. Under the optimized conditions obtained from ANN-GA, the method exhibited a linear dynamic range of 30-3000ngmL-1 with a detection limit of 5.70ngmL-1. The preconcentration factor and enrichment factor were 57.47 and 93.54, respectively with relative standard deviations (RSDs) less than 4.0% (N=6). The interference effect of some ions and dyes was also investigated and the results show a good selectivity for this method. Finally, the method was successfully applied to the preconcentration and determination of Maxilon Red GRL in water and wastewater samples.Entities:
Keywords: Artificial neural network-genetic algorithm; Central composite design; CoFe(2)O(4)-NPs-AC; Determination; Maxilon Red GRL; Ultrasound assisted
Year: 2016 PMID: 27544797 DOI: 10.1016/j.ultsonch.2016.08.012
Source DB: PubMed Journal: Ultrason Sonochem ISSN: 1350-4177 Impact factor: 7.491