l-Cystine-functionalized graphene oxide nanosheets for effective extraction and preconcentration of mercury ions from environmental waters

In the present study, graphene oxide (GO) nanosheets were functionalized withl-cystine (GO@Cystine), a natural and stable sulfur-containing amino acid, via a simple one-pot process. The synthesized adsorbent was used to selectively extract and concentrate mercury (Hg) ions from aqueous solutions before their determination by cold vapor atomic absorption spectrometry. To achieve maximum performance, key parameters affecting the extraction process were investigated and optimized. The results showed that 30 mg GO@Cystine was capable of extracting Hg ions at pH = 3.0, with an average efficiency of 96%. Due to the strong binding of Hg ions to the adsorbent surface, the quantitative elution with HCl solution was possible at concentrations above 5 mol L-1. The linear calibration curve in the concentration range of 0.1-10.0 ng mL(-1)(r = 0.9974), the limit of detection of 13 ng L-1, and the relative standard deviations of 3.3% and 2.7% were achieved at 0.5 and 2.5 ng mL(-1), respectively. The adsorbent capacity and the maximum preconcentration factor were 98.3 mg g(-1)and 125, respectively. The proposed method was able to extract and measure mercury ions in aqueous environmental samples, such as river water and seawater. The extraction recoveries of the spiked standard solutions (2.5 ng mL(-1)) ranged from 94.8 to 97.2%. Therefore, along with other common adsorbents, the use of GO@Cystine as a highly potent, low-cost, simple, environmentally friendly, and selective adsorbent is recommended.

Automated summary

In this study, graphene oxide nanosheets functionalized with l-cystine were successfully synthesized and used as an efficient adsorbent for selective extraction and concentration of mercury ions from aqueous solutions. The method showed a high efficiency in extracting and measuring mercury ions, with excellent linear calibration curve and accuracy achieved through optimization of key parameters.