Removal of molybdenum using silver nanoparticles from water samples: Particle swarm optimization–artificial neural network
Removal of molybdenum using silver nanoparticles from water samples: Particle swarm optimization–artificial neural network
复制标题
DOI：
10.1016/j.jiec.2013.11.036
复制
发表时间：
2014-09
影响因子：
6.1
通讯作者：
M. Khajeh;K. Dastafkan
中科院分区：
文献类型：
--
作者：
M. Khajeh;K. Dastafkan
采用纳米银固相萃取法和紫外可见分光光度法，建立了一种简便、快速的水样中痕量钼的预富集测定方法。将人工神经网络-粒子群优化算法(ANN-PSO)用于固相萃取法的模拟和优化，建立了预测模型。在最佳条件下，方法的检出限为11μg/L−1，相对标准偏差为3.9%。该方法的预富集系数为50。方法用于水样中钼的预富集和测定。
In this study, a simple and fast method for preconcentration and determination of trace amount of molybdenum from water samples was developed by silver nanoparticles based solid-phase extraction method and UV–vis spectrophotometry. Hybrid of artificial neural network–particle swarm optimization (ANN–PSO) has been used to develop predictive models for simulation and optimization of solid phase extraction method. Under the optimum conditions, the detection limit and relative standard deviation were 11 μg L−1and <3.9%, respectively. The pre-concentration factor of this method was 50. The method was applied to preconcentration and determination of molybdenum from water samples.