Modelling and optimization of factors influencing adsorptive performance of agrowaste-derived Nanocellulose Iron Oxide Nanobiocomposites during remediation of Arsenic contaminated groundwater

Abstract

Nanocellulose Iron Oxide Nanobiocomposites (NIONs) were synthesized from rice husk and sugarcane bagasse derived nanocelluloses for adsorptive removal of arsenic and associated contaminants present in groundwater samples. These NIONSs were superparamagnetic, hence magnetically recoverable and demonstrated promising recyclability. Synthesis of NIONs was confirmed by Transmission electron microscopy (TEM), X-Ray Diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopic (XPS). FTIR and XPS data together with adsorption kinetics provide insights into probable adsorption mechanism of Arsenic by NIONs. The experimental conditions for 10 different variants were modelled using response surface methodology (RSM) based on central composite design (CCD), considering the parameters; adsorbate dosage, adsorbent dosage, pH and contact time. The results identified the best performing variants and the optimal conditions for maximal absorption (~99%). These results were validated using a three-layer feed-forward Multilayer Perceptron (MLP) based Artificial Neural Network (ANN) model. Both RSM and ANN chemometric models were in close conformity for optimized conditions of highest adsorption by specific variants. The standardized conditions were used to expand the study to field-based arsenic contaminated groundwater samples and their performance to commercial adsorbents. NIONs show promising commercial potential for water remediation applications due to their high adsorptive performance, magnetic recoverability and recyclability.