Abstract
Hexavalent chromium [Cr (VI)] is a highly toxic and hazardous contaminant that poses serious health risks to both humans and the environment. Its presence in water sources can lead to severe health issues, including various types of cancer and respiratory ailments. Therefore, developing efficient and effective methods for Cr (VI) removal is crucial in ensuring safe and clean water supplies. The aim of this research is the environmentally responsible elimination of hexavalent chromium by bioadsorption using corn residues (CR), palm fiber (PF), and the peels of yam (YP), cassava (CP), and cocoa (CH). The study was conducted with varying levels of pH, bioadsorbent quantity, temperature, and adsorbent particle size at 200 rpm, with an initial concentration of 100 mg/L and 24 h of contact time to improve the adsorption efficiency. The process variables were evaluated and optimized using the statistical technique response surface methodology (RSM). The SEM-EDS analysis revealed that the predominant elements in the structure of the bioadsorbents were carbon and oxygen. Furthermore, the adsorption process led to the incorporation of Cr (VI) into the structure of the biomaterials, as indicated by their EDS spectra. The maximal adsorption efficiency of 99.11% was obtained at pH 2, bioadsorbent dose of 0.03 mg, 30 °C, and 0.5 mm of particle size. Various equilibrium isotherms were utilized to fit and analyze the adsorption data. The assessed maximum adsorption capacities were 38.84, 56.88, 52.82, 138.94, and 240,948.7 mg/g for YP, PF, CP, CH, and CR, respectively. The adsorption data exhibited conformity with the Freundlich and Redlich–Peterson isotherm models (R2 = 0.95), indicating that the phenomenon occurs in a multilayer. Pseudo-second order and Elovich kinetic models adjusted the kinetics of chromium (VI), suggesting that the mechanism could be controlled by chemisorption. Therefore, the residual biomasses evaluated can serve as a cost-effective adsorbent for Cr (VI) removal, and the use of RSM enables efficient modeling and prediction of the adsorption process.
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