Experimental and Modeling Optimization of Strontium Adsorption on Microbial Nanocellulose, Eco-friendly Approach

Abstract

Green synthesized cellulose nanocrystals (CNCs) was prepared using Neurospora intermedia, characterized, and used to remove Strontium ions (Sr2+) from an aqueous solution with high efficiency. The characterization of CNCs was performed using a UV-Vis Spectrophotometer, Dynamic Light Scattering (DLS), Zeta Potential (ZP), Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) mapping, EDX elemental analysis and BET surface analyzer. In this study, Response Surface Methodology (RSM) based on Box-Behnken Design (BBD) was successfully applied for the first time to optimize the dynamic adsorption conditions for the maximum removal of Sr2+ ions from aqueous solutions using CNCs as adsorbent. The effects of parameters, such as initial concentration of Sr2+ (50–500 ppm), adsorbent dosage (0.05–0.2 g/50ml), and contact time (15–120 min.) on removal efficiency were investigated. A mathematical model was studied to predict the removal performance. The significance and adequacy of the model were surveyed using the analysis of variance (ANOVA). The results showed that the second-order polynomial model is suitable for the prediction removal of Sr2+ with regression coefficient (R2 = 97.41%). The highest sorption capacity value of Sr2+ was obtained (281.89 mg/g) at the adsorbent dosage of 0.05 g/50 ml, contact time of 120 min., and the pollutant (Sr2+) concentration of 275 ppm.