Akademik Veri Yönetim Sistemi
Turkish Journal of Analytical Chemistry (Online), cilt.7, sa.2, ss.108-131, 2025 (Hakemli Dergi)
This study investigates the adsorption of Pb(II) ions from aqueous solutions onto pine bark (Pinus brutia Ten.) using nonlinear regression analysis to evaluate kinetic and equilibrium data. Adsorption experiments were conducted over a range of initial concentrations, and the equilibrium data were fitted to various isotherm models, including Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich (D-R), as well as advanced three-parameter models like Tóth, Sips, Redlich-Peterson (R-P), and Brouers-Sotolongo (B-S). Kinetic data were analyzed using pseudo-first order (PFO), pseudo-second order (PSO), Elovich, Avrami, and B-S models. Nonlinear regression, facilitated by Microsoft Excel Solver, was used to optimize model parameters, and goodness-of-fit was assessed through multiple error functions, including SSE, ARE, HYBRID, MPSD, and MAE. Results indicate that the Brouers-Sotolongo (B-S) model provided the best fit for both kinetic and isotherm data, reflecting the heterogeneous surface characteristics of the adsorbent. The adsorption process was found to involve a combination of physical and chemical interactions, as evidenced by the kinetic constants (αBS and nBS) and the half-reaction time (τ1/2). Among the equilibrium models, three-parameter isotherms, particularly the B-S, Tóth, and Sips models, showed superior performance over two-parameter models, highlighting the complex nature of adsorption mechanisms in this system. This study underscores the efficacy of pine bark as a low-cost and eco-friendly adsorbent for heavy metal removal and demonstrates the utility of nonlinear regression and advanced error analysis in adsorption studies. This approach is thought to improve the precision of model selection and the understanding of adsorption mechanisms, contributing to the literature.