Investigating the impact of multiple background species on the reactivity of GSH@Fe3O4 employing RSM-CCD design tool for the sorption of a model anionic dye.

The major setback during wastewater treatment is the competition from several background ions and organic acids, hindering adsorbent-pollutant interactions and reducing removal efficiency. To address this issue, it is essential to find a promising methodology that can optimize ion effects, predict adsorbents' best removal efficiency, and determine significant influencing ions. Thus, the present study employed RSM-CCD design-based methodology with five input variables (HCO₃^-, SO₄^2-, NO₃^-, Cl^-, and humic acid) to simulate and optimize various wastewater environments, so as to investigate the reactivity of GSH@Fe₃O₄ NPs for phenol red (PR) dye. The quadratic model was found to be in best agreement with the experimental data, with an R² of 0.98. The other statistical tests and diagnostic plots also confirmed that the above model is highly reliable, accurate and provides good precision. The highest removal in a multicomponent system was predicted to be 83.09% when all the dominating species were kept at optimal points. Moreover, the experimental data fitted well for pseudo-second-order reaction, elucidating chemisorption as the major adsorption mechanism. When tested with real wastewater matrices, the PR dye removal followed the order: tap water > paper and pulp wastewater > sewage > textile wastewater. The findings of this study can be very instrumental in strategizing the wastewater conditions and enhancing removal efficiency in treatment plants while contributing towards real-world applications and sustainable wastewater treatment practices.