The emergent occurrence of sulfonamide species involving sulfadiazine (SDZ) and sulfamethazine (SMZ) in aquatic systems can cause a wide range of potential risks; hence, remediation strategies need to be necessary. Here, we develop the novel metal-organic framework-derived nanocomposite, and apply for the adsorption of SDZ and SMZ antibiotics. To assess the best-fitting kinetic (pseudo first-order, pseudo second-order) and isotherm (Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, Redlich-Peterson, Sips, Toth, and Khan) models, a series of numerous statistical analysis was performed. Numerous error functions including squares of the errors (SSE), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), and mean relative error (MRE) were also analyzed to assess the linear and nonlinear models. The results indicated that both linear and nonlinear kinetic models were mostly fitted well with pseudo second-order models (Radj)2 > 0.97. Although linear kinetics gave better (Radj)2, error functions (MRE, SSE, HYBRID, and MPSD) were mostly higher than those of nonlinear kinetics. For adsorption isotherm, nonlinear Redlich-Peterson was the most compatible model with extremely high adjusted coefficients of determination (Radj)2 ~ 1.0000. While nonlinear Langmuir model gave relatively high (Radj)2 (0.9898-0.9960) and acceptable error functions, we found the considerable difference of error functions and parameters among four types of linear Langmuir (Types I, II, III, IV). The findings indicate potential errors as selecting one of linearized Langmuir types in equilibrium study. It is suggested that nonlinear models should be applied for better fitness.
Keywords: Fe3O(BDC)3; Function errors; Mesoporous carbon nanocomposite; Metal-organic framework; Nonlinearized modeling; Sulfonamide antibiotic remediation.
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