Optimizing Fe0/Ni0/alginate beads as a stable and recoverable catalyst for removing highly toxic water contaminants: full-factorial design

Abstract

Pollution by highly toxic contaminants poses a great threat to the aquatic environment and human life. Bimetallic materials have been proven to be efficient for the removal of such contaminants. In this study, the bimetallic Fe0/Ni0/alginate beads have been prepared using solvothermal technique followed by polymerization of alginate. Full-factorial design has been utilized to optimize the preparation conditions including the weight ratios of Fe and Ni, and time of the solvothermal process. The bimetal made from equal weights of Ni and Fe metals along with alginate has shown the best efficiency for the removal of 2,4,6-trichlorophenol (2,4,6-TCP, ~ 70%) as a model organic pollutant under acidic conditions (pH = 2) with minor contribution of alginate component in the removal process. Adsorption followed by reduction has been defined as the removal mechanism using GC/MS/MS chromatography in the case of 2,4,6-TCP. A mathematical model was established to describe the 2,4,6-TCP removal by the optimally prepared material. Differently, Pb2+ ions were removed predominantly by the alginate with minor effect of the bimetal (max. removal = 96.91% at pH 5 and after 2 h contact time). Accordingly, it was difficult to describe this process by a mathematical model. However, in both cases, the optimally prepared material has exhibited superior stability and reusability rather than the neat Fe0/Ni0 bimetal under the same experimental conditions. To sum up, the designed bimetallic Fe0/Ni0/alginate beads can be considered as an efficient catalyst for the removal of highly toxic pollutants from aqueous media.

A Box-Behnken design-based chemometric approach to optimize the degradation of hydroxychloroquine in water media using the Fe(0)/S2O2-8/UV system

“…A common element of these methods is the opportunity of varying all optimization-relevant factors at the same time; these variations are made in a way that is efficient in the search and by taking into account a possible interaction between the operating factors (Dall'Agnol et al 2020). The experimental design optimization methods such as Doehlert design (Ghrib et al 2019), Taguchi(Rahman and Raheem 2022), Full Factorial (Maria et al 2020;Radwan et al 2022b), Central composite (Homem et al 2010;Eslami et al 2016),…”