Linear and nonlinear kinetic and isotherm adsorption models for arsenic removal by manganese ferrite nanoparticles
In the present work, we analyzed the linear and nonlinear model suitabilities for adsorption data from aqueous As(III) removal by manganese ferrite nanoparticles (NPs). Hence, As(III) adsorption onto ferrite NPs was formerly analyzed by the intraparticle diffusion model (IPD). Then, adsorption kinet...
Saved in:
Published in | SN applied sciences Vol. 1; no. 8; p. 950 |
---|---|
Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Cham
Springer International Publishing
01.08.2019
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | In the present work, we analyzed the linear and nonlinear model suitabilities for adsorption data from aqueous As(III) removal by manganese ferrite nanoparticles (NPs). Hence, As(III) adsorption onto ferrite NPs was formerly analyzed by the intraparticle diffusion model (IPD). Then, adsorption kinetics was described by the pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich models, while equilibrium adsorption was fitted to the Freundlich and Langmuir isotherms. Linear and nonlinear kinetic and isotherm models were solved and compared. The nonlinear data fitting was applied through the
lsqcurvefit
user-defined function (Matlab ver. 7.10.0). The initial adsorption rate was influenced by intraparticle diffusion and surface or film diffusion from the arsenic bulk solution to ferrite NPs, according to the IPD model. Adsorption kinetics of As(III) on manganese ferrite NPs was better described by the PSO model, followed by the Elovich model and then the PFO model. Equilibrium adsorption data were only worthily described by the Freundlich isotherm model. While the PSO, Elovich and Freundlich linear models showed even better fit than the nonlinear models, determinant bias was depicted for the PFO and Langmuir linear models. Thus, to use nonlinear adsorption models is highly advisable, having the Matlab
lsqcurvefit
function been proven very useful to face such task. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 2523-3963 2523-3971 |
DOI: | 10.1007/s42452-019-0977-3 |