Removal of molybdenum using silver nanoparticles from water samples: Particle swarm optimization–artificial neural network |
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| Affiliation: | 1. Department of Environmental Engineering, Konkuk University, Seoul 143-701, Korea;2. Green Technology Co., Ltd., 6F Hanam Venture Tower B/D, ChangWoo-Dong, HaNam-Si 465-120, GyeongGi-Do, Korea;3. Department of Advanced Technology Fusion, Konkuk University, Seoul 143-701, Korea;1. Physical Chemistry Department, Laboratory of Surface Chemistry and Catalysis, National Research Center, Dokki, Cairo, Egypt;2. Zoology Department, College of Science, King Saud University, Riyadh 11451, Kingdom of Saudi Arabia;3. Electron Microscope and Thin Films Department, National Research Center (NRC), El- Behooth Street, Giza 12622, Egypt |
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| Abstract: | 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−1 and <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. |
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| Keywords: | Molybdenum Silver nanoparticles Artificial neural network Particle swarm optimization |
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