Vortex assisted dispersive solid phase extraction of thallium followed by electrothermal atomic absorption spectrometry, Adsorption mechanism and soft computing algorithm prediction

Abstract

ABSTRACT Vortex-assisted dispersive solid-phase extraction technique was used as a rapid and efficient method for preconcentration of ultra-trace levels of total thallium (Tl) followed by electrothermal atomic absorption spectrometry. Graphene oxide-modified polyvinylpyrrolidone nanocomposite was prepared as an adsorbent and characterised by the Fourier transform infrared spectrometry, field emission-scanning electron microscopy, energy-dispersive X-ray spectroscopy , and X-ray diffraction spectroscopy. To find the optimum conditions for extraction of ultra-trace levels of Tl (ӀӀӀ), the response surface methodology based on central composite design was used. Based on the results, pH = 6.6, amounts of adsorbent = 7.7 mg, extraction time = 27 min and desorption time = 5 min provide maximum extraction recovery for Tl (ӀӀӀ). Under the optimum conditions, the calibration curve was linear in the range of 0.08–1.5 µg L−1 Tl (ӀӀӀ) with the R 2 value of 0.9985. The relative standard deviation was 4.0% (n = 7) and the detection limit was 0.019 µg L−1 (n = 8). Also, the enrichment factor which could be calculated from the slope of the calibration curve after preconcentration step to that without preconcentration was 95.8. To understand the adsorption mechanism, two-parameter and three-parameter adsorption isotherms were studied, and the obtained results show that the adsorption of Tl (ӀӀӀ) followed by the Freundlich isotherm and the maximum adsorption capacity was 142.8 mg g−1. Also, the results of adsorption kinetic show that the adsorption of Tl (ӀӀӀ) was followed by the pseudo-first-order kinetic model. Moreover, random tree (RT) and artificial neural network (ANN) are employed for prediction of adsorption performance based on effective parameters. The outcomes of soft computing demonstrated that RT (R 2 = 0.95) and ANN (R 2 = 0.87) have acceptable accuracy for estimation and prediction of extraction recovery of Tl (ӀӀӀ).