Magnetically-derived pecan nut shells for the adsorptive removal of cadmium: Artificial neural network modelling and photodegradation of sulfamethoxazole using the spent sorbent

This research investigated the potential of a nanocomposite, PeN@Fe₃O₄, derived from pecan nut shells, for cadmium (Cd²⁺) removal from water. The magnetically derived PeN@Fe₃O₄ was characterised by FTIR (Fourier Transform infrared spectroscopy), SEM (Scanning electron microscope), EDX (Energy dispersive x-ray), TEM (Transmission electron microscope), XRD (X-ray diffraction spectroscopy) and BET (Brunauer-Emmett-Teller) analyses. The material demonstrated exceptional adsorption capacity, reaching 63.29 mg/g. Also, the adsorbent achieved 90.21 % efficiency when tested with a real wastewater sample. Kinetic studies revealed that Cd²⁺ adsorption is best suited to the pseudo-second-order model, while the Freundlich isotherm is the best-fit model for the adsorption equilibrium. Notably, the Cd²⁺-laden PeN@Fe₃O₄ was successfully reused as a photocatalyst, achieving almost complete degradation of the antibiotic sulfamethoxazole. To further optimise the process of adsorption, an artificial neural network (ANN) model was built. This model 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. 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. The ANN model offers a valuable tool for predicting and optimising adsorption parameters, maximising Cd²⁺ removal and contributing to the production of clean water. This innovative approach minimises secondary pollution associated with spent adsorbent disposal, paving the way for a more environmentally friendly remediation strategy.