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Superior Graphene oxide-Cu2SnS3-PANI (GO-CTS-PANI) adsorbent for highly efficient treatment of mercury ion from water resources; Experimental Practices, Classical and Artificial Intelligence Modelling
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  • Sara Enferadi,
  • Mohammad Eftekhari,
  • Mohamad Gheibi,
  • Nikoo Nabizadeh Moghaddam,
  • Stanislaw Wacławek,
  • Kourosh Behzadian
Sara Enferadi
Ferdowsi University of Mashhad
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Mohammad Eftekhari
University of Neyshabur
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Mohamad Gheibi
Technical University of Liberec
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Nikoo Nabizadeh Moghaddam
Ferdowsi University of Mashhad
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Stanislaw Wacławek
Technical University of Liberec
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Kourosh Behzadian
University of West London

Corresponding Author:[email protected]

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Abstract

This study presents a new type of adsorbent, Graphene oxide-Cu2SnS3-Polyaniline (GO-CTS-PANI) composite that was synthesised and used for the removal of mercury ions (Hg2+) from water sample. The adsorbent was characterised using various techniques including Fourier transform infrared spectrophotometry (FT-IR), Field Emission Scanning Electron Microscopy (FESEM), Energy-dispersive X-ray spectroscopy (EDX), Elemental Mapping analysis and X-ray diffraction analysis (XRD). The Box-Behnken method was used to optimise the key factors affecting the adsorption process, including pH, amount of adsorbent, and contact time with the application of Design Expert Version 7.0.0. Results showed the adsorption process was highly efficient at pH 6.5, 12 mg of GO-CTS-PANI adsorbent, and 30 minutes of contact time, achieving a maximum removal percentage of 95% for 50 mg/L Hg2+ ions. The study investigated the isotherm and kinetic of the adsorption process which showed the adsorption process occurred in sequential layers (Freundlich isotherm) and was continued by a physical interaction between the adsorbent and adsorbate. The pseudo second order kinetic equation is an appropriate model to interpret of kinetic data. The Response Surface Methodology (RSM) assessment indicated that pH was the most effective parameter for maximising the adsorption efficiency. The study also applied artificial intelligence methods such as Random Forest algorithm due to enhancement of adsorption process efficiency prediction and compare their performance to classical models. The findings show the Random Forest algorithm was highly accurate, achieving a correlation coefficient of 0.98. This study highlights the potential of GO-CTS-PANI composite for efficient removal of Hg2+ ions from water resources.