Abstract
The advancement of technology and industrial activities has led to the release of considerable amounts of hazardous pollutants, specifically heavy metals, into aquatic ecosystems. Their presence in water resources is a major concern as these toxic substances are not biodegradable and tend to accumulate in biological tissues, posing serious risks to human health and the environment. The remediation of metal contaminated water is a critical environmental challenge, particularly in removing toxic heavy metals such as lead, zinc, copper and nickel. This research focuses on the synthesis and application of novel three dimensional (3D) graphene oxide (GO) based composites for efficient removal of these metals from aqueous solutions.The initial investigation focused on the synthesis and batch adsorption of 3D GO composite enhanced with aluminium sulfate (GOCAS) for the removal of lead and zinc. Through response surface methodology (RSM) optimisation, GOCAS demonstrated high adsorption capacities of 138.7 mg/g for lead and 52.69 mg/g for zinc. The adsorption process was best described by pseudo-second-order model (PSO), Freundlich model (lead) and Langmuir model (zinc). Furthermore, GOCAS exhibited good reusability, maintaining over 50% adsorption performance after four cycles of regeneration. The practicality of GOCAS was assessed dynamically in a packed bed. This included studies on the effect of bed height, influent concentration and flowrate, which yielded maximum dynamic capacities of 148.93 mg/g for lead and 111.38 mg/g for zinc. The breakthrough profiles were accurately modelled by the fractal-like Thomas model which validated the structural and chemical heterogeneity of GOCAS within the dynamic system.
On the other hand, GSn, a 3D GO composite enhanced with stannum oxide for the removal of copper and nickel. RSM optimisation resulted in adsorption capacities of 87.57 mg/g for copper and 75.96 mg/g for nickel. The adsorption kinetics for both metals were represented by PSO model. Regeneration studies further confirmed the reusability and stability of GSn across four cycles. Dynamic packed bed adsorption of copper and nickel using GSn was subsequently evaluated. Maximum adsorption capacities of 67 mg/g for copper and 59.27 mg/g for nickel were obtained. The dynamic data were best described by the fractal-like Clark model which successfully predicted the non-ideal kinetic behaviour as well as the high heterogeneity of GSn.
Finally, the study examined the complexities of real-world scenarios by evaluating the performance of GSn in binary solutions of copper and nickel. Competitive adsorption was observed in batch and packed bed column experiments, with greater impact on nickel. Kinetic studies were consistent with the single component kinetics, best fitted by PSO model. The Extended Freundlich model accurately described the simultaneous adsorption on the heterogeneous surface of GSn. Packed bed experiments demonstrated the displacement of nickel by copper, highlighting a higher selectivity for copper. This selectivity was attributed to the smaller ionic radius, higher electronegativity and Misono softness of copper.
Conclusively, this study demonstrates the successful development of aluminium- and stannum-functionalised 3D GO composites with high adsorption capacities, structural robustness and reusability. These materials outperform conventional adsorbents and offer scalable, sustainable solutions for industrial wastewater treatment.
| Date of Award | 18 Jul 2026 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Yuanyuan Shao (Supervisor) & Lai Yee Lee (Supervisor) |
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