Biosorption optimization of lead(II) and cadmium(II) ions by two novel nanosilica-immobilized fungal mutants.

Abstract

This study aims at immobilization of fungal mutants on nanosilica (NSi)-carriers for designing efficient biosorbents as a significant new technology for decontamination practices and maximizing their heavy metal (HM) sorption proficiency through the experimental design methodology. Endophytic fungal mutant strains, Chaetomium globosum El26 mutant and Alternaria alternata S5 mutant were heat inactivated and then immobilized, each separately, on NSi carriers to formulate two separated nano-biosorbents. The formulated NSi-Chaetomium globosum El26 mutant (NSi-Chae El26 m) was investigated for Pb+2 uptake while, the formulated NSi-Alternaria alternata S5 mutant (NSi-Alt S5 m) was investigated for Cd+2 uptake, each through a batch equilibrium protocol. Before and after the metal sorption process, the designed nano-biosorbents were characterized via scanning electron microscopy, energy-dispersive X-ray spectroscopy, and Fourier transform infrared analysis. Sorption pH, contact time, sorbent concentration, and initial HM concentration were statistically optimized using a Box-Behnken design. Results showed that NSi-Chae El26 m was efficient in Pb+2 uptake with maximum biosorption capacities of 199.0, while NSi-Alt S5 m was efficient in Cd+2 uptake with maximum biosorption capacities of 162.0mg g-1 . Moreover, the equilibrium data indicated that the adsorption of Pb+2 and Cd+2 by the tested nano-biosorbents fitted to the Freundlich isotherm. The formulated nano-biosorbents resulted in higher HM biosorption of metal ions from aqueous solution than that obtained by the free fungal biomass. The biosorption statistical modelling described the interactions between the tested sorption parameters and predicted the optimum values for maximum HM biosorption capacity by the two designed nano-biosorbents. These findings verify that members of the endophytic fungal genera Alternaria and Chaetomium are suitable to produce nano-biosorbents for decontamination practices after treatment by gamma mutagenesis, heat inactivation, and NSi immobilization. Moreover, statistical optimization can assist to evaluate the optimal conditions to produce such bioremediation material.