Nonlinear regression for treating adsorption isotherm data to characterize new sorbents: Advantages over linearization demonstrated with simulated and experimental data.

ABSTRACT: This paper demonstrates that determining adsorption capacity and affinity through data fitting of adsorption isotherms by nonlinear regression (NLR) is more accurate than linearized Langmuir equations. Linearization errors and the subjective choice of data points used to apply the linear regression analysis may deviate the fitted adsorption parameters (constants and adsorption capacities) from the expected values. The deviation magnitude increases for heterogeneous sorbents such as environmental particles and molecularly imprinted polymers, which adsorb by more than one sorption mechanism or adsorption sites of diverse chemical natures. For instance, Lineweaver-Burk linearization of isotherms simulated considering the presence of two adsorption sites (distinct adsorption energies) provides

SUBMITTER: Vitek R 

PROVIDER: S-EPMC10112023 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature