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Modelling and optimising the performance of graphene oxide-Cu2SnS3-polyaniline nanocomposite as an adsorbent for mercury ion removal

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Abstract

Finding a cost-effective, efficient, and environmentally friendly technique for the removal of mercury ion (Hg2+) in water and wastewater can be a challenging task. This paper presents a novel and efficient adsorbent known as the graphene oxide-Cu2SnS3-polyaniline (GO-CTS-PANI) nanocomposite, which was synthesised and utilised to eliminate Hg2+ from water samples. The soft–soft interaction between Hg2+ and sulphur atoms besides chelating interaction between -N and Hg2+ is the main mechanism for Hg2+ adsorption onto the GO-CTS-PANI adsorbent. Various characterisation techniques, including Fourier transform infrared spectrophotometry (FT-IR), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), elemental map** analysis, and X-ray diffraction analysis (XRD), were employed to analyse the adsorbent. The Box-Behnken method, utilising Design Expert Version 7.0.0, was employed to optimise the crucial factors influencing the adsorption process, such as pH, adsorbent quantity, and contact time. The results indicated that the most efficient adsorption occurred at pH 6.5, with 12 mg of GO-CTS-PANI adsorbent, and 30-min contact time that results in a maximum removal rate of 95% for 50 mg/L Hg2+ ions. The study also investigated the isotherm and kinetics of the adsorption process that the adsorption of Hg2+ onto the adsorbent happened in sequential layers (Freundlich isotherm) and followed by the pseudo-second-order kinetic model. Furthermore, response surface methodology (RSM) analysis indicates that pH is the most influential parameter in enhancing adsorption efficiency. In addition to traditional models, this study employed some artificial intelligence (AI) methods including the Random Forest algorithm to enhance the prediction of adsorption process efficiency. The findings demonstrated that the Random Forest algorithm exhibited high accuracy with a correlation coefficient of 0.98 between actual and predicted adsorption rates. This study highlights the potential of the GO-CTS-PANI nanocomposite for effectively removing of Hg2+ ions from water resources.

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Authors and Affiliations

  1. Department of Chemistry, Faculty of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

    Sara Enferadi & Nikoo Nabizadeh Moghaddam

  2. Department of Chemistry, Faculty of Sciences, University of Neyshabur, Neyshabur, Iran

    Mohammad Eftekhari

  3. Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Studentská 1402/2, 461 17, Liberec, Czech Republic

    Mohammad Gheibi & Stanislaw Wacławek

  4. School of Computing and Engineering, University of West London, St Mary’s Rd, London, W5 5RF, UK

    Kourosh Behzadian

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  1. Sara Enferadi
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  2. Mohammad Eftekhari
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  3. Mohammad Gheibi
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  4. Nikoo Nabizadeh Moghaddam
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  5. Stanislaw Wacławek
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Contributions

Sara Enferadi: data curation, methodology; Mohammad Eftekhari: data curation, methodology, supervision, conceptualisation, writing—review and editing; Mohammad Gheibi: methodology, software, writing—review and editing; Nikoo Nabizadeh Moghaddam: data curation, methodology; Stanislaw Wacławek: writing—review and editing, and supervision; Kourosh Behzadian: data curation and conceptualisation, supervision, writing—review and editing.

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Correspondence to Kourosh Behzadian.

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Enferadi, S., Eftekhari, M., Gheibi, M. et al. Modelling and optimising the performance of graphene oxide-Cu2SnS3-polyaniline nanocomposite as an adsorbent for mercury ion removal. Environ Sci Pollut Res 31, 38196–38216 (2024). https://doi.org/10.1007/s11356-024-33746-4

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