Characterization of Sorption Isotherms, Kinetic Models, and Multivariate Approach for Optimization of Hg(II) Adsorption onto Fraxinus Tree Leaves

Abstract

Statistical experimental design was utilized to optimize removal of aqueous Hg(II) by Fraxinus tree leaves through a batch biosorption process. Sorbent−sorbate behavior was evaluated by fitting equilibrium data by nonlinear and transformed linear forms of the Langmuir, Freundlich, and Redlich−Peterson isotherms. The comparative study showed that nonlinear regression is a better way to model equilibrium data. A 23 full factorial design was used to identify significant factors and interactions. The pH, Hg(II) initial concentration, and sorbent mass were examined as major factors. The contact time was fixed at 30 min. All of the factors were significant at the 95 % confidence level. The amount of Hg(II) was determined by cold vapor atomic absorption spectrophotometry. A regression model was derived by using a response surface methodology through performing central composite design (CCD). Model adequacy was checked by such diagnostic tests as analysis of variance (ANOVA), lack of fit test, residuals distribution, and over-fitting test. On the other hand a residuals distribution was evaluated for normality according to the Ryan−Joiner test. As a result, the optimized condition for Hg(II) biosorption was calculated to be pH = 4.4, s = 0.25 g, and m = 50 mg·L−1, which corresponds to 92.25 % removal efficiency. The biosorption process was kinetically fast and followed a pseudosecond order kinetic model. Fourier transform infrared (FT-IR) and X-ray diffraction (XRD) spectra were used to find more about the biosorption mechanism.

Cercis siliquastrum Tree Leaves as an Efficient Adsorbent for Removal of Ag(I): Response Surface Optimization and Characterization of Biosorption

“…The mechanism of metal-binding in biosorption process is various, and is thought to include ion-exchange, surface adsorption, chemisorptions, and adsorption-complexation [7,8,15,18,19]. The plant cell walls mainly consist of polysaccharides, proteins, and lipids.…”

“…The multivariate optimization has several advantages over conventional ''one-at-atime'' method because of providing more information through running fewer experiments [15][16][17][18][19][20][21][22][23][24]. During multivariate optimization, a response surface methodology (RSM) leads to an empirical mathematical model, which relates the removal percent of metal ion with effective variables and their interactions [17][18][19][21][22][23][24].…”

“…For this purpose the central composite design (CCD) as a response surface approach is developed for finding out the optimum conditions and maximum removal of Ag(I) from aqueous solution [15,18,19]. Apart from that, the determination of proton binding active sites at different pHs on the biosorbent along with recognition of the mechanism of Ag(I) removal from aqueous solutions using Cercis siliquastrum tree leaves as a biosorbent are pursued [25][26][27].…”

Uptake of Zn2+ and As3+ from Wastewater by Adsorption onto Imine Functionalized Magnetic Nanoparticles

“…Linear least square regression routine in the origin software statistical computing systems was used to fit the experimental data into the kinetic and isotherm models [31]. The origin statistical software considers the adjustment of the regression coefficient square, R 2 and the minimization of the residual sum of squares (RSS).…”

“…Adsorbents dose of 40 mg were agitated with a 20 mL aliquot of Zn(II) and As(III) on a thermostated oven shaker at temperature ranges of 293, 303, 313 and 318 K for 4 h. The solutions were separated magnetically and the concentration of Zn(II) and As(III) in the filtrate determined by using AAS. The experimental adsorption data were fitted into two isotherm models; Langmuir and Freundlich models [30][31][32]. Table 2 shows the equations of the models.…”