Abstract
This thesis presents a comprehensive study on the development and application of agricultural waste-derived magnetic and zinc oxide nanocomposites for the removal of toxic pollutants from water. Three distinct systems were engineered: magnetically derived pecan nutshells (PeN@Fe3O4) for cadmium ion removal, cetyltrimethylammonium bromide-modified magnetic apricot shells (Fe3O4@Ap/CTAB) for Congo red dye (CRD) removal and diethylenetriamine functionalised zinc oxide - apricot stone shell nanocomposite (ZnO@AP/DETA) for sequestering 2,4-dichlorophenoxyacetic acid (2,4-D). All materials were synthesised using environmentally sustainable approaches, and the materials alongside the precursors were characterised to evaluate their structural, morphological, and surface properties. A blend of analytical techniques, viz, powder X-ray diffraction using (XRD), Thermal gravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), and Brunauer–Emmett–Teller (BET) were employed.
The first part reports the removal of Cadmium (II) ion from synthetic and real wastewater spiked with cadmium using magnetically derived pecan nutshells. The material demonstrated exceptional adsorption capacity, reaching 63.29 mg/g with a removal efficiency of 90.21% when tested with a real wastewater sample spiked with Cd(II). Notably, the Cd2-laden PeN@Fe3O4 was successfully reused as a photocatalyst, achieving almost complete degradation of the antibiotic sulfamethoxazole. This innovative approach minimises secondary pollution associated with spent adsorbent disposal, paving the way for a more environmentally friendly remediation strategy. To further optimise the process of adsorption, an artificial neural network (ANN) model, which accurately predicted Cd2+ removal efficiency and capacity, achieving a mean absolute error (MAE) of 0.10906, mean squared error (MSE) of 0.04399, and a high R2 value of 0.99975, was built. The findings highlight that PeN@Fe3O4 is a promising candidate for environmental remediation. Its ability to effectively remove Cd2+ and subsequently degrade organic pollutants in a closed-loop system provides an environmentally friendly wastewater treatment solution.
Also, in the second part, the synthesis of a cetyltrimethylammonium bromide-modified magnetic (Fe3O4@Ap/CTAB) nanocomposite from abundantly available apricot shells was reported. The composite displayed a point of zero charge (pHpzc) at 8.04, with surface area
viii
and the pore volume of 18.7842 m²/g and 0.089736 cm³/g, respectively. The Fe3O4@Ap/CTAB nanocomposite displayed a capacity of 37.175 mg g-1 at 25 °C and 44.053 mg g-1 at 45 °C, at pH 6.5, with a dosage of 50 mg and a concentration of 22.3 mgL-1 in 4 hours. It achieved a 95.77% removal efficiency from real wastewater spiked with CRD after shaking for 180 minutes at 25 °C under natural wastewater conditions. A multi-layer ‘feed-forward neural network’, was developed to effectively predict CRD removal efficiency with a high R2 value of 0.9587 and low MAE of 1.7626. The minimal relative percentage error between the predicted and the test data confirms the ANN's effectiveness in capturing the non-linear behaviour of CRD removal. In order to remove as much CRD as possible from aqueous solutions using Fe3O4@Ap/CTAB, this model of ANN can be employed for the prediction and optimisation of the adsorption process parameters for maximising CRD removal.
Lastly, a novel nanocomposite, diethylenetriamine functionalised zinc oxide-apricot stone shell (ZnO@AP/DETA), has been synthesised for the efficient sequestration of 2,4-dichlorophenoxyacetic acid (2,4-D). The nanocomposite displayed a more uniform mesoporous structure having pores with an average size of 34.0659 nm and a significantly enhanced surface area of 26.5622 m²/g, approximately 13 times greater than that of the pristine apricot stone (AP) shells, which have 2.0764 m²/g. The ZnO@AP/DETA was utilised to adsorb 2,4-D from synthetic and real wastewater samples, achieving performance efficiencies of 98.6% and 85.41%, respectively, after a 90-minute agitation period at 25 °C. An ANN model was developed, which successfully predicted 2,4-D removal efficiency with MAE of 0.2952, MSE of 0.4227, and a high R2 of 0.9991. These results highlight the potential of ZnO@AP/DETA for environmental remediation and its role in wastewater treatment.
In summary, this study reports the successful engineering of three nanocomposites and employs them to tackle the issues of heavy metals and organic compounds' resistance to conventional methods. These novel materials have enabled the resolution of issues related to recalcitrance, high operational costs of existing methods and the problems associated with the reuse or disposal of spent adsorbents. This work explores the dual functionality of the nanocomposites for adsorption and photocatalytic degradation after use, with the integration of artificial neural network (ANN) modelling for predictive performance analysis. Owing to their outstanding efficiency in removing Cd2+, Congo red dye and 2,4-D, and the potent degradation capability of the Cd2+-saturated used adsorbent towards SMX, PeN@Fe3O4, Fe3O4@Ap/CTAB, and ZnO@AP/DETA nanocomposites emerge as a viable material for environmental restoration.
The re-adaptation of the fully spent Cd-loaded adsorbent for the photocatalytic degradation of sulfamethoxazole (SMX) adds a dual-purpose plan to the purification of water. Such a combined solution can therefore be termed as being in line with resource recovery and circular economy management of the environment. The deployment of the ANN framework in predicting the adsorption process parameters at the same time that the adsorption performance is enhanced provides an opportunity to actually perform process optimisation in-real time by allowing making informed operational decisions based on predicting adsorption capacity and efficiency effectively over a wide scope of operating conditions, thus making the use of the enormous trial-and-error methods to test the process less critical and therefore enabling the reduction in resources use to operate the system. As a result, the combination of intelligent modelling with the advanced sorbent engineering executed in this thesis tackles both economic and technical limitations of usual water treatment practice, which makes adsorption treatment a more sustainable and feasible method of pollutant removal