Enferadi, Sara, Eftekhari, Mohammad, Gheibi, Mohammad, Nabizadeh Moghaddam, Nikoo, Wacławek, Stanislaw and Behzadian, Kourosh ORCID: https://orcid.org/0000-0002-1459-8408 (2024) Modelling and Optimising the Performance of Graphene Oxide-Cu2SnS3-Polyaniline nanocomposite as an Adsorbent for Mercury Ion Removal. Environmental Science and Pollution Research.

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Abstract

Finding a cost-effective, efficient and environmentally friendly technique for removal of mercury ion (Hg2+) in water and wastewater can be a challenge 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 mercury ions (Hg2+) from water samples. The soft–soft interaction between Hg2+ and sulfur atoms besides chelating interaction between -N and Hg2+ and also electrostatic interaction are the main mechanisms 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), Elemental Mapping 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 a 30-minute contact time, achieving a maximum removal rate of 95% for 50 mg/L Hg2+ ions. The study also explored the isotherm and kinetics of the adsorption process, revealing that adsorption took place in sequential layers (Freundlich isotherm) and was followed by a physical interaction between the adsorbent and the adsorbate. The pseudo second-order kinetic equation proved to be a suitable model for interpreting the kinetic data. Furthermore, Response Surface Methodology (RSM) analysis indicated that pH was the most influential parameter in enhancing adsorption efficiency. In addition to traditional models, this study employed artificial intelligence methods, such as the Random Forest algorithm, to enhance the prediction of adsorption process efficiency. The findings demonstrated that the Random Forest algorithm exhibited high accuracy, achieving a correlation coefficient of 0.98. Overall, this research underscores the potential of the GO-CTS-PANI composite for effectively removing Hg2+ ions from water resources.

Item Type:	Article
Depositing User:	Kourosh Behzadian
Date Deposited:	12 Jun 2024 11:58
Last Modified:	04 Nov 2024 11:17
URI:	https://repository.uwl.ac.uk/id/eprint/12004

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