Removal of molybdenum using silver nanoparticles from water samples: Particle swarm optimization–artificial neural network

• The nanoparticles have large specific area and internal diffusion resistance is absent.
• The nanoparticles have a higher efficiency for the removal of molybdenum.
• The hybrid ANN–PSO was used to optimize the extraction percent of molybdenum.
• Determination of the molybdenum in water samples was successfully performed.

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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