Modeling ionic strength effects on cation adsorption at hydrous oxide/solution interfaces

Abstract

An efficient material is required to develop effective removal systems with high flexibility and low cost for capturing toxic ions. In this study, a synthesized lanthanum oxide-modified bentonite (B-La) adsorbent was used for the adsorption of As(V) and Pb(II). The prepared material, B-La, shows a feather shape surface with a 25 nm average particle size and was also confirmed by SEM and TEM. Texture and chemical composition confirmed by XRD and FTIR. From the XPS investigation, the underlying mechanism for Pb(II) and As(V) removal was investigated, and the production of B-La-As(V) and B-La-Pb(II) inner-sphere complexation was shown to be the dominating pathway. As per the pH effect studies, it was observed that B-La efficiently adsorbed Pb(II) and As(V) over a pH range of 6.0. Pb(II) adsorption was not substantially impacted by ionic strength or coexisting ions; however, As(V) adsorption was influenced by phosphate and fluoride, showing that B-La has a strong affinity towards Pb(II) and As(V). A pseudo-second-order model could well fit Pb(II) and As(V) adsorption, and Langmuir equilibrium adsorption describes Pb(II) and As(V) adsorption. The adsorption capacity was 147.05 mg of Pb(II)/g and 156.26 mg of As(V)/g, reflected at the dosage of 0.2 g/L at 25 °C, pH 6.0. Using the response surface methodology, the optimum values for process parameters, such as initial concentration, temperature, pH, and time, for both Pb(II) and As(V) removal are identified. Desorption studies were also performed to confirm the presented B-La adsorbents' environmental suitability. The Pb(II) and As(V) were completely desorbed with 0.2 M HNO3 and 0.2 M NaOH; after washing with water, it is ready to reuse in the next cycle and can be reused in several cycles of Pb(II) and As(V) adsorption operations. The identified data-driven quadratic model strongly correlated with the experimental values. Machine-learning techniques like artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS), based on results ANN predicted model shows high correlation R2 value for both metal ions. All the findings indicated that porous B-La was a promising material for Pb(II) and As(V) removal.