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Abstract

In the present work, an economical, more selective and a bio-compatible fluoride removal technique has been developed by using zirconium impregnated Chlorella protothecoides and Nannochloropsis oculata, which represents a combination of biological and physiochemical methods. The optimization of the effect of various factors such as pH, initial fluoride concentration, biosorbent dose and contact time was carried using a central composite design of response surface methodology. A maximum removal of fluoride ions of 92.2% was observed using zirconium-doped C. protothecoides. The adsorption isotherms and kinetics of the adsorption process were studied. The Langmuir isotherm model well expressed fluoride biosorption onto zirconium-doped N. oculata and the Freundlich isotherm model fitted well the data of zirconium-doped C. protothecoides. The correlation coefficients indicate that the fluoride biosorption onto zirconium-doped N. oculata, and C. protothecoides biosorbents correlated well with the pseudo-second-order kinetic model. The existence of co-anions decreased fluoride removal from aqueous solution. The biosorbent was regenerated using NaOH, distilled water and HCl and used for four cycles.

Keywords

Biosorption defluoridation Zirconium CCD

Introduction

Fluoride prevents dental caries at lower concentrations but at concentrations above 1.5 ppm, it causes serious health problems to humans and animals through drinking water (Ayoob and Gupta, 2006; Maiti et al., 2011). Fluoride enters the groundwater due to slow dissolution of fluorine-containing rocks (Agarwal et al., 2003; Bhatnagar et al., 2011; Sujana et al., 2009). Industries like glass and ceramic, semiconductor, electroplating, coal-fired power stations, beryllium extraction plants, brick and iron works and aluminum smelters increase the fluoride contamination in groundwater (Bhatnagar et al., 2011).

Defluoridation of water using both physico-chemical and biological methods together have proven advantageous when compared to specific methods (Mekonen et al., 2001). Researchers have employed plain algae Spirogyra IO2 and algal biomass of Anabaena fertilissima and Chlorococcum humicola pre-treated with Ca²⁺ for the biosorption of fluoride from polluted waters (Bhatnagar et al., 2011). Algae, with many functional groups on the cell walls, are capable of bonding with different metal ions (Evans et al., 1987) and act as a carrier of metal ions. Several composite materials, such as Ce-Al (Tang and Zhang, 2016), Ca-Al-La (Xiang et al., 2014), Zn-Al (Das et al., 2003) and, Ce-Zr (Ghosh et al., 2014) have been tried for the removal of fluoride by adsorption. Zirconium being a tetravalent ion has been found useful in fluoride and arsenic removal from aqueous solutions (Viswanathan and Meenakshi, 2008). Hydrated zirconium produces tetranuclear and octanuclear ions. The hydroxyl ions and water molecules present on the zirconium ions undergo ligand substitution with arsenic and fluoride anions (Biswas et al., 2008; Zhang et al., 2012).

In this work, we prepared an eco-friendly, novel biosorbent by impregnating zirconium (IV) onto algae, and investigated its fluoride adsorption behaviour. Besides, the effects of key operation parameters, such as initial fluoride concentration, fluoride solution pH, biosorbent dose, contact time and coexisting anions were optimized using a central composite design (CCD) based on response surface methodology (RSM). The RSM is an experimental modelling system that evaluates the relation between the experimentally controlled group of variables and an obtained response (Vasconcelos et al., 2000). In addition, the isotherms and kinetics of the adsorption process were studied, and the possible mechanism of fluoride removal by the adsorbent was also proposed based on the Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscope–energy dispersive spectroscopy (SEM-EDS) and X-ray photoelectron spectroscopy (XPS) analysis results.

Experimental procedure

Materials

Media and analytical chemicals. All chemicals and reagents used in the study were of analytical grade and procured from Sigma–Aldrich, USA. Synthetic fluoride samples were used throughout the study.

Analysis of fluoride ion concentration

The concentration of fluoride ion in the solution was determined by Mettler Toledo fluoride ion selective electrode (perfectION™ combined fluoride electrode make) and Mettler Toledo ion analyzer (SevenCompact pH/ion meter S220 make). A 5 mL of TISAB III (total ionic strength adjustment buffer, pH 5–5.5) was added to 50 mL fluoride solutions to prevent the interference of other ions with the fluoride measurements.

The instrument was calibrated using fluoride standards provided by Mettler Toledo to make working solutions of required concentration. These solutions were used to calibrate the instrument to measure fluoride concentrations.

Procurement and culturing of algal strains

Chlorella protothecoides (SAG 211-10C), and Nannochloropsis oculata (SAG 38.85) were procured from EPSAG, Göttingen, Germany. The composition of Bold’s basal medium for C. protothecoides cultivation is as given in Table 1. Bold’s basal medium (modified) was used for the cultivation of N. oculata. The composition of Bold’s basal medium (modified) and the composition of trace metal solution are given in Tables 2 and 3, respectively.

Table 1.

Composition of Bold’s basal medium.

Component	mg/L	Component	mg/L
Co(NO₃)₂.6H₂O	4.9	EDTA	500
CuSO₄.5H₂O	15.7	KH₂PO₄	1250
MnCl₂.4H₂O	14.2	FeSO₄.7H₂O	49.8
H₃BO₃	114.2	MgSO₄.7H₂O	1000
ZnSO₄.7H₂O	88.2	KNO₃	1250

Table 2.

Composition of Bold’s basal medium (modified).

Component	Stock solution (mL)	mL/L	Component	Stock solution	mL/L
KH₂PO₄	8.75 g/500	10	Na₂EDTA.2H₂O	10 g/L	1
CaCl₂.2H₂O	1.25 g/500	10	KOH	6.2 g/L	1
MgSO₄.7H₂O	3.75 g/500	10	FeSO₄.7H₂O	4.98 g/L	1
NaNO₃	12.5/500	10	H₂SO₄	—	1
NaCl	1.25/500	10	Trace metal solution	—	1

Table 3.

Composition of trace metal solution.

Component	Quantity (g/L)	Component	Quantity (g/L)
H₃BO₃	2.86	Na₂MoO₄.2H₂O	0.390
MnCl_2.4H₂O	1.81	CuSO₄.5H₂O	0.079
ZnSO₄.7H₂O	0.222	Co(NO₃)₂.6H₂O	0.0494

Cultivation of algae and preparation of the algal powder

The 300 mL of sterile medium in 1-L Erlenmeyer flasks were inoculated with algal cells and placed inside an illumination box at 24℃. The light intensity was maintained in the box by using a suitable number of fluorescent tube lights. The algal cells were exposed to 12:12 h of light:dark cycle. The growth rates of the cells were monitored by measuring the biomass at regular intervals. After the exponential growth phase, the cells were transferred to fresh medium. The volume of the medium was increased in a stepwise manner to get more biomass. Finally, the biomass was harvested by allowing the cells to settle completely and decanting the clear liquid. Then the biomass was transferred to a separating funnel, and the excess water was separated out. The biomass was dried in a hot-air oven maintained at 70℃ for 24 h. The dried biomass was then ground to a fine powder and stored in an air-tight container.

Preparation of algal biosorbents by zirconium impregnation

In this study, the powdered algal species were doped with zirconium by adding 5% ZrOCl₂.10H₂O solution to the algal powder in the ratio of 3:1 (wt ratio). This ratio was fixed based on the optimum removal efficiency of adsorbent as indicated in online Figure SI-1. The mixture was equilibrated for 72 h at 25℃. The zirconium-doped algae were then filtered, washed with water to remove free zirconium ions, dried in an oven at 100℃ and subsequently used to investigate its defluoridation capacity (Rajan and Alagumuthu, 2013).

Characterization of zirconium-impregnated algal biomass

The chemical groups of the samples before and after fluoride biosorption were characterized using FT-IR spectroscopy (Spectrum Two™ from PerkinElmer, USA). The surface morphology of the samples was observed using scanning electron microscope (SEM, Ultra 55 FESEM model from Carl Zeiss). Elemental analysis of the samples was carried out using energy dispersive spectroscopic (EDS) detector from Oxford Instruments. Kratos Axis Ultra spectrometer (UK) with Al Kα anode radiation source was used for XPS studies. The binding energies in XPS studies were referenced to the C1s peak of the surface adventitious carbon at 284.8 eV. Mastersizer 2000 (Malvern Instruments, Inc., UK) was used to determine the particle size of the biosorbent. Brunauer–Emmett–Teller (BET) surface area was analysed using TriStar 3000 V6.05 A.

Zeta potential measurement

The zeta potential (ζ) of the surface was found as per a previously reported procedure (Zhang, 2005) for 10 g/L of biosorbent suspension with 100 ppm fluoride and without fluoride, in the pH range of 2–10 using a Zetasizer 2000 (Malvern Instruments, Inc.). The 0.01 mol/L NaNO₃ was added as the background electrolyte and aged for 24 h at 25℃. The initial pH was adjusted using HNO₃ or NaOH. Biosorbent suspensions with or without F⁻ at the preferred pH were shaken at 25℃ and 180 r/min for 24 h. The final pH was analysed, and then the suspension was introduced into the electrophoretic cell for determining the zeta potential values. The pH at the point of zero charge was determined by interpolating the zeta potential data to the zero potential (Dou et al., 2012).

Batch biosorption experiments

The batch studies were conducted for the optimization of pH, initial fluoride concentration, biosorbent dosage and contact time. The experiments were performed at 30℃ in 250-mL polypropylene flasks. The initial pH of the samples was maintained using 0.1 M HCl or 0.1 M NaOH. A known amount of biosorbent was added to the feed solution, and the samples were shaken in a rotary shaker incubator for a known period at 190 r/min. The samples were then filtered using Whatman filter paper no. 42 and the clear solution was analysed to determine the residual fluoride concentration using the fluoride ion selective electrode.

The percentage biosorption was calculated using equation (1).

% biosorption = \frac{(C_{0} - C_{e})}{C_{0}} * 100

(1) where C₀ and C_e (mg/L) refers to the concentrations of fluoride ion present initially and at equilibrium in the sample, respectively.

Experimental design and data analysis

The optimization of the four variables, that is pH (2.0–8.0), initial fluoride concentration (10–100 mg/L), adsorbent dosage (1–10 g/L) and contact time (30–240 min) were conducted through CCD of RSM. The response function of interest was percentage biosorption, which was approximated by a second-degree polynomial equation using the method of least squares (Arteaga and Li-chan, 1994). Design Expert Version 9.0.3.1 (Stat-Ease, MA) was employed for optimizing the process variables and also to assess the effects and mutual interactions of each process variable. The initial batch biosorption studies were useful in determining the levels of the different process parameters. The design consisted of a total of 30 runs with six replicates, and the α value was considered as one by selecting the face-centred option. The design with three levels namely, low, medium and high, was coded as −1, 0 and +1. For the statistical calculations, the four-factors, the range and the levels used in the trial are recorded in Table 4. The major effects and the relations between factors were established. The experiments were performed based on the experimental design matrix obtained by CCD, and the results were analysed using response plots and analysis of variance (ANOVA). ANOVA was useful in recognizing the effects of individual variables on fluoride removal by biosorption (Myers and Montgomery, 2002; Kristo et al., 2003). The behaviour of the system was elucidated using second-degree polynomial as shown by equation (2).

Y = β_{0} + \sum_{i}^{k} β_{i} x_{i} + \sum_{ii}^{k} β_{ii} x_{i}^{2} + \sum_{i < j}^{k} β_{ij} x_{i} x_{j}

(2) where Y is the response; β₀ is a constant, β_i are linear coefficients, β_ij are second-order interaction terms and β_jj are quadratic coefficients; the variables x_i and x_j are non-coded independent variables. Since four variables are involved in the present study, k = 4.

Table 4.

The experimental factors and levels for the CCD.

Factor	Low level (−1)	Medium level (0)	High level (+1)
Initial F⁻ concentration, ppm	10	55	100
Biosorbent dosage, g/L	1	5.5	10
pH	2	5	8
Contact time, min	30	135	240

The percentage fluoride removal was fitted into equation (2) and ANOVA was also performed to obtain a correlation between the independent variables and the response. The coefficient of determination, R² depicted the goodness-of-fit of the polynomial model and the F-test illustrated the statistical significance. The duplicate measurements helped in determining the pure error, residual error and lack-of-fit (Myers and Montgomery, 2002). A maximum desirability was selected for fluoride removal at optimum pH, initial fluoride concentration, adsorbent dosage and contact time. The 3D surface plots represented the mutual interaction between the levels for each of the variable and the responses.

Equilibrium studies

Adsorption isotherm experiments were carried out in 250-mL polypropylene flasks on 100 mL of solution containing fixed initial fluoride concentrations as 100 ppm. The adsorbent dose was varied from 1 to 10 g/L. The pH was adjusted and held at pH 2. These flasks were agitated at 190 r/min and maintained at 30℃ for 4 h. The solutions were filtered, and analysed for the residual fluoride concentrations.

Adsorption kinetics

A 10 g sample of the algal biosorbent in 250-mL polypropylene flasks containing 100 mL of fluoride solution (100 ppm) were mixed and placed in a temperature-controlled rotary shaker maintained at 190 r/min and sampled at different time intervals. The biosorbent was finally removed by filtration and the fluoride concentration was determined. The contact time required for complete fluoride biosorption was determined for each biosorbent.

Desorption studies

To increase the feasibility and economy of the biosorption method, the regeneration of the biosorbents were studied. The adsorption–desorption cycles of fluoride were repeated four times with 1% NaOH, distilled water and 0.1 N HCl. In batch desorption experiments, 10 g of fluoride-loaded biosorbent in 250 mL Erlenmeyer flasks was contacted in series with 100 mL of 1% NaOH, 80 mL of distilled water and 100 mL of 0.1 N HCl solutions consecutively, at room temperature (27℃ ± 2℃). Each mixture was agitated on orbital shaker at 160 r/min for 30 min. The biosorbent was removed and supernatant was analysed for fluoride concentration by ion selective electrode.

Results and Discussion

Characterization of the biosorbents

The particle size of zirconium-doped C. protothecoides and N. oculata algal biosorbents, their BET surface area, and specific gravity are shown in Tables 5 and 6, respectively.

Table 5.

Particle size of the algal biosorbents.

Biosorbent	D (v, 0.1), µm	D (v, 0.5), µm	D (v, 0.9), µm
Chlorella protothecoides-Zr	0.26	2.50	6.98
Nannochloropsis oculata-Zr	0.68	15.32	162.15

Table 6.

BET surface area and specific gravity.

Biosorbent	BET surface area (m²/g)	Specific gravity
Chlorella protothecoides-Zr	16.05	2.389
Nannochloropsis oculata -Zr	31.91	1.765

Figure 1(a) and (b) depict the SEM images of zirconium-doped C. protothecoides before and after fluoride sorption, and Figure 1(c) and (d) represent those for N. oculata, respectively. In Figure 1(a) and (c), the surface morphology of the natural algal sorbent were shown, and no well-defined pores were observed. In Figure 1(b) and (d), no significant changes on the algal sorbent were observed after fluoride sorption. This may be due to low concentrations of fluoride. The elemental constituents of zirconium-doped C. protothecoides and N. oculata before and after fluoride-sorption were determined using EDS analysis. The analysis results for zirconium-doped C. protothecoides before and after fluoride biosorption are depicted in Figure 2(a) and (b), and that for zirconium-doped N. oculata before and after fluoride-sorption are displayed in Figure 3(a) and (b), respectively. It was noted that in the fluoride-laden samples (Figures 2(b) and 3(b)) the fluoride was present in small amounts (2.01 wt %) along with the other elements, namely, O, Mg, P, Cl, K, Ca, Fe and Zr.

Figure 1.

SEM images of zirconium-doped Chlorella protothecoides (a) before and (b) after fluoride adsorption and Nannochloropsis oculata biomass (c) before and (d) after fluoride adsorption at 50.00 K × 200 nm.

Figure 2.

EDS of Chlorella protothecoides + Zr biosorbent (a) before and (b) after fluoride adsorption.

Figure 3.

EDS of Nannochloropsis oculata + Zr biosorbent (a) before and (b) after fluoride adsorption.

The FTIR spectra of zirconium-doped C. protothecoides and N. oculata algal biosorbents before and after fluoride sorption are shown in Figure 4(a) and (b), respectively. FTIR spectra recorded peaks at 3500 cm⁻¹ that is attributed to the overlapping of –NH₂ and –OH stretching vibrations (Viswanathan et al., 2009); at 2850 cm⁻¹ due to C–H stretching (Amin et al., 2015); and at 1724 and 1654 cm⁻¹ due to the involvement of C = C or C = O groups. The peaks at 1558 and 1457 cm⁻¹ may be attributable to N–H bending in the amine group and at 1033 cm⁻¹ due to C–O stretching of carboxylic acids. The peaks at 525 cm⁻¹ show stretching of crystalline Zr–O bonds (Sarkar et al., 2007). The FTIR peaks of biomass after fluoride biosorption showed a reduction in wave number for –NH₂ groups and also a slight broadening of the –NH₂ stretching band due to hydrogen bonding between the protonated amine and fluoride ions (Smith, 1998). Therefore, zirconium, amine, carboxylic and hydroxyl groups influenced fluoride removal to a large extent.

Figure 4.

FTIR spectra of zirconium-doped (a) Chlorella protothecoides and (b) Nannochloropsis oculata biosorbent before and after adsorption.

The XPS wide spectra of the zirconium-doped C. protothecoides and N. oculata algal biomass, before and after adsorption are displayed in Figure 5(a) and (b), respectively. Several peaks were found in the XPS spectra and were identified as C, Ca, K, O, Fe, P and Zr. It was observed that the peaks of Zr and P elements modified after fluoride adsorption. The enlarged spectra highlighting zirconium (Zr3d) and phosphorus (P2s) elements before and after fluoride biosorption for zirconium-doped C. protothecoides and N. oculata are shown in Figure 5(a) and (b), respectively. Before fluoride adsorption, prominent peaks from Zr3d_3/2, Zr3d_5/2 and P2s were located at 187, 184.52 and 191.2 eV, respectively. After fluoride adsorption, the Zr3d_3/2, Zr3d_5/2 and P2s peaks broadened and a shoulder appeared for these peaks, on careful observation these can be assigned to metal-fluorine bondings. This suggests the formation of new bond between zirconium and phosphorus species with fluoride ion (Zr–F, P–F). As explained by Dou et al., this is what one can expect if zirconium is subjected to greater electron withdrawal when it becomes bonded to fluorine (Dou et al., 2012).

Figure 5.

XPS plot of (a) Chlorella protothecoides (CP) + Zirconium before and after adsorption at pH 2, (b) Nannochloropsis oculata (NO) + Zirconium before and after adsorption at pH 2.

The new species responsible for the peak shifting was determined by using peak deconvolution method on the Zr3d spectra before and after adsorption. The method reported by Zhang et al. (1995) was used by employing a computer algorithm which varied the relative intensity (peak area) and binding energy position of two identical Zr3d peaks to generate a ‘best fit’ to the experimental data. The resolved spectra are given in Figure 6(a) to (d) and summarized in Table 7. During the fitting process, the peak area ratio remained identical for the two corresponding doublets, that is A(Zr3d_3/2 doublet for Zr-F)/A(Zr3d_3/2 doublet for Zr-O) = A(Zr3d_5/2 doublet for Zr-F)/A(Zr3d_5/2 doublet for Zr-O) (where A means peak area).

Figure 6.

Deconvoluted XPS spectra of Zr3d and P2s for (a) Chlorella protothecoides (CP) + Zirconium before and (b) after adsorption at pH 2, (c) Nannochloropsis oculata (NO) + Zirconium before and (d) after adsorption at pH 2.

Table 7.

The deconvolution of Zr3d and P2s spectra for the zirconium ion before and after fluoride adsorption.

Adsorbent	Parameters	Species	Peaks	BE^a (eV)	Area	FWHM^b (eV)	%GL
Chlorella protothecoides (CP) + Zirconium	Before adsorption	Zr-O Zr-O P-O	3d_5/2 3d_3/2 2s	184.52 187.00 191.20	2074.02 529.495 5696.73	2.23 1.49 3.77	19 12 35
Chlorella protothecoides (CP) + Zirconium	After adsorption	Zr-O Zr-F Zr-O-F P-O P-F	3d_5/2 3d_5/2 3d_3/2 2s 2s	183.77 185.99 188.51 191.00 193.43	3841.47 1295.01 1003.99 5673.35 1464.21	3.40 2.22 1.95 2.81 2.28	20 14 15 21 20
Nannochloropsis oculata (NO) + Zirconium	Before adsorption	Zr-O Zr-O P-O	3d_5/2 3d_3/2 2s	183.29 185.87 191.75	5859.72 2487.13 2074.88	3.54 3.38 3.69	20 20 10
Nannochloropsis oculata (NO) + Zirconium	After adsorption	Zr-O Zr-F P-O P-F	3d_5/2 3d_5/2 2s 2s	183.61 186.66 190.47 193.00	8034.80 2050.97 1586.79 1001.26	3.50 3.10 2.78 2.69	27 0 13 19

Binding energy (BE).

The full width at half maximum (FWHM).

Gaussian:Lorentzian.

The zeta potential of the zirconium-doped algal biomass in the absence (0 ppm F⁻) and the presence of fluoride (100 ppm F⁻) are depicted in Figure 7. The point of zero charge values obtained by linear interpolation of the experimental data is 4.5 and 6 for pure zirconium-doped C. protothecoides and N. oculata biosorbents, respectively. The point of zero charge values were small (3.8 and 4.8) for fluoride-laden algal biomass. The reduction in point of zero charge values with increasing fluoride concentration may be due to chemical bond formation between Zr–F structures (Dou et al., 2012; Goldberg and Johnston, 2001). The algal biomass surface gets protonated under acidic conditions, and the affinity between the biomass and fluoride ions increases due to electrostatic attraction.

Figure 7.

Zeta potential values of the zirconium-doped (a) Chlorella protothecoides (b) Nannochloropsis oculata before and after fluoride adsorption, that is 0 and 100 ppm F⁻solution; adsorbent dose, 10 g/L; at 25℃.

Equilibrium studies

The equilibrium concentrations in the solution (C_e) were determined after the system reached equilibrium while the concentrations in the adsorbent (q_e) were calculated as in equation (3).

q_{e} = \frac{(Co - Ce) V}{M}

(3) q_e is the amount of fluoride adsorbed per unit weight of the sorbent at equilibrium (mg/g), C_e is the equilibrium concentration of fluoride in solution (mg/L), V is the volume of the fluoride solution (L) and M is the mass of algal biosorbent (g).

The data so obtained were used to determine which model fits best (Tan et al., 2008). The four adsorption models employed in the present study were: the Freundlich, Langmuir, Dubinin–Radushkevich (D-R) and Temkin adsorption.

The Freundlich model is given by equation (4) and represents adsorption on a heterogeneous surface (Tan et al., 2008).

q_{e} = K_{f} {C_{e}}^{(\frac{1}{n})}

(4) K_f and 1/n are Freundlich constants and are measures of adsorption capacity (mg/g) and adsorption intensity or surface heterogeneity. The Freundlich isotherm constants K_f and n were calculated from the slope and the intercept of the plot of ln q_e versus ln C_e (Figure 8) and were presented in Table 8. The surface heterogeneity is higher for zirconium-doped C. protothecoides with the 1/n value of 0.7176 than zirconium-doped N. oculata with the 1/n value of 0.9378. The surface heterogeneity increases on the decrease in the 1/n value. The better fit for Freundlich isotherm for the sorption of the fluoride onto the zirconium-doped C. protothecoides biomass implies non-ideal sorption on heterogeneous surfaces as well as multilayer sorption (Tan et al., 2008).

Figure 8.

Adsorption isotherm plots of (a) Langmuir, (b) Freundlich, (c) D–R and (d) Temkin models for zirconium-doped Chlorella protothecoides (CP) and Nannochloropsis oculata (NO) biomass.

Table 8.

Adsorption isotherm constants of Freundlich, Langmuir, D–R and Temkin models for zirconium-doped Chlorella protothecoides and Nannochloropsis oculata biomass.

Isotherm	Parameters (constants)	Zirconium-doped algal biosorbents
Isotherm	Parameters (constants)	Chlorella protothecoides-Zr	Nannochloropsis oculata-Zr
Freundlich isotherm	Slope:1/n	0.7176	0.9378
	Intercept: ln K_f	0.7067	0.3736
	n	1.3935	1.0663
	K_f (mg/g)	2.0272	0.6882
	R ²	0.9765	0.9227
Langmuir isotherm	Slope: 1/q_mK_L	0.443	1.4815
	Intercept: 1/q_m	0.031	0.0114
	q_m (mg/g)	31.53	87.37
	K_a (L/mg)	13.973	129.43
	R_L	0.000715	7.73E-05
	R ²	0.718	0.9567
D-R isotherm	Slope: B_D	−5E-06	−4E-05
	Intercept: ln (q_s)	3.1114	3.2155
	q_s (mg/g)	22.45	24.91
	B_D (mol²/kJ²)	5E-06	4E-05
	E_D (kJ/mol)	310.08	111.8
	R ²	0.3571	0.7198

The Langmuir adsorption isotherm model is given by equation (5) (Tan et al., 2008).

q_{e} = \frac{q_{m} K_{a} Ce}{1 + K_{a} Ce}

(5)

The adsorbent uptake capacity (q_m) is the amount of adsorbate present to form a monolayer (mg/g), and K_a (L/mg) is the Langmuir constant that relates to the energy of adsorption. The K_a and q_m values were calculated from the slope and the intercept of the plot 1/q_e versus 1/C_e (Figure 8 and Table 8). The values of the Langmuir separation factor, R_L obtained from equation (6), indicates whether the adsorption is either unfavourable when R_L > 1, linear when R_L = 1, favourable when 0 < R_L < 1 or irreversible if R_L = 0.

R_{L} = \frac{1}{1 + K_{a} C_{0}}

(6)

Based on the correlation coefficient values, the equilibrium data of fluoride ion biosorption were best described by the Langmuir equation for zirconium-doped N. oculata. The maximum sorption capacity at the expense of minimum energy was evident from the values of q_m and R_L values of 87.37 mg/g and 129.43 L/mg. Since the R_L value lies between 0 and 1, the adsorption is favourable and suggests monolayer coverage of fluoride on the zirconium-doped N. oculata (Tan et al., 2008).

D-R isotherm was applied to understand the adsorption mechanism in terms of Gaussian energy distribution onto a heterogeneous surface. The linearized D-R isotherm equation is represented by equation (7) (Tan et al., 2008).

\ln q_{e} = \ln q_{m} - β ɛ^{2}

(7) where ɛ is the Polanyi potential, and is equal to R_T ln(1 + 1/C_e), β is the constant related to adsorption energy, R is universal gas constant, and T is the temperature in Kelvin. D-R isotherm constants β and q_m were calculated from the slope and intercept of the plot ln q_e versus ɛ² (Figure 8 and Table 8). E, the mean free energy of sorption required to transfer one mole of an adsorbate to the surface from infinity in solution was determined using equation (8).

E = 1 / \sqrt{- 2 β}

(8)

The Temkin isotherm was applied to take into account the adsorbent–adsorbate interactions. The model ignores the extremely low and significant value of concentrations and assumes that the heat of adsorption (a function of temperature) of all molecules in the layer would decrease linearly rather than logarithmically with coverage (Tan et al., 2008).

The model is represented by the equation (9).

q_{e} = A_{T} + B_{T} ln C_{e}

(9)

A_T and B_T are the Temkin isotherm equilibrium binding constant (L/g) and Temkin isotherm constant. The constants A_T and B_T were determined from the slope and intercept of the plot q_e against ln C_e (Figure 8)_. Since the R² values of the plot are very less, the Temkin constants are not mentioned in Table 8.

Adsorption kinetics

The mechanism of adsorption can be explained using kinetic constants obtained from different kinetic models. To investigate the sorption mechanism of the algal biosorbents, the first-order, pseudo-second-order, Elovich and intra-particle diffusion kinetic models were selected.

The kinetics of fluoride adsorption was analysed by the first-order rate expression, which is used when the liquid and solid phases attain equilibrium conditions (Chhipa et al., 2013). The first order rate equation is given by equation (10).

\frac{1}{q_{t}} = \frac{1}{q_{1}} + \frac{k_{1}}{q_{1} t}

(10) Here, q_t is the amount of adsorbed fluoride per unit weight of biosorbent, mg/g; t is the time, min; q₁ is the amount of fluoride adsorbed in mg/; k₁ is the first order rate constant, min⁻¹.

The pseudo-second-order kinetic model considers that there is chemical bond formation between the ion and the adsorbent surface. The linear form of the model is given by equation (11).

\frac{t}{q_{t}} = \frac{1}{k_{2} q_{2}^{2}} + \frac{t}{q_{2}}

(11) where q₂ is the maximum sorption capacity, mg/g; q_t is the amount of fluoride adsorbed at equilibrium, mg/g; t is the contact time, min; k₂ is the pseudo-second order chemisorption (g/mg.min). q₂ and k₂ values were obtained from the intercept and slope of the plot of t/q_t versus t.

Elovich equation considers the sorbent surface to be energetically heterogeneous and is primarily used for chemisorption process (Chhipa et al., 2013). The linearized equation is given by equation (12).

q_{t} = β ln (α β) + β lnt

(12) where, α is the sorption rate (mmol/(g min)) and β is the desorption constant (g/mmol).

The batch studies involve vigorous shaking, which may cause bulk transport of fluoride ions into the pores of the biosorbent as well as biosorption at the outer surface of the sorbent. There is a possibility of either film diffusion or intra-particle diffusion to be controlling the adsorption rate. Hence, the flow of fluoride ions into the pores of the biosorbent was tested by the intra-particle diffusion or the Weber and Morris model. The intra-particle diffusion model by Weber and Morris is represented by equation (13) (Chhipa et al., 2013).

q_{t} = k_{i} t^{1 / 2} + C

(13) where k_i is the intra-particle diffusion rate constant (mg/g min).

The pseudo-second-order kinetic model has shown highest regression coefficients for both zirconium-doped C. protothecoides and N. oculata biosorbents, while the other three kinetic models did not fit well (Figure 9). The values of different parameters of the rate model equations were attained from the slope and intercept of the kinetic model plots shown in Figure 9. The parameters of pseudo-second-order kinetic model are mentioned in Table 9.

Figure 9.

Adsorption kinetic models (a) first-order (b) pseudo-second-order (c) Elovich (d) intra-particle diffusion models for the sorption of fluoride by zirconium-doped Chlorella protothecoides (CP) and Nannochloropsis oculata (NO) biomass.

Table 9.

Adsorption kinetic parameters for the adsorption of fluoride by zirconium-doped Chlorella protothecoides and Nannochloropsis oculata biomass.

Adsorption kinetic models	Chlorella protothecoides-Zr	Nannochloropsis oculata-Zr
Pseudo-second-order model
Rate constant, K₂ (g/mg.min)	0.0420	0.03
Constant, q₂ (mg/g)	9.4808	8.5465
R ²	0.9992	0.9979

Statistical analysis

The central composite design matrix was used to determine the effect of variables: pH (2.0–8.0), initial fluoride concentration (10–100 mg/L), adsorbent dosage (1–10 g/L) and contact time (30–240 min) on the response (R₁). R₁ represents the biosorption of fluoride ion with zirconium-doped C. protothecoides and N. oculata biomass.

Validation of response surface models and statistical analysis

Second-order polynomial equations drew the relationship between independent variables and response. The regression equation coefficients were evaluated and fitted to a second-order polynomial equation for fluoride removal using zirconium-doped algal strains C. protothecoides and N. oculata.

The empirical relationship between the response and process variables was obtained using one of the statistical testing tools called F-test. The significance of regression model was determined using ANOVA for biosorption of fluoride using zirconium-doped C. protothecoides and N. oculata biomass. The results of ANOVA for the removal of fluoride ions using zirconium-doped C. protothecoides and N. oculata biomass are shown in online Tables SI-1 and SI-2. The model terms that are significant for the biosorption of fluoride ions values are indicated with Prob > F value less than 0.0001. The lack-of-fit shown as insignificant indicate that the obtained quadratic model is applicable for the present study. The predicted R²= 0.9790 and the adjusted R²= 0.9933 for zirconium-doped C. protothecoides; the predicted R²= 0.9734 and the adjusted R²= 0.9914 for zirconium-doped N. oculata are in reasonable agreement and closer to 1.0. This confirms that the model fits the experimental data well.

Optimization of variables for the removal of fluoride ions

The effect of independent variables: pH, initial fluoride concentration, adsorbent dosage and contact time, on the biosorption of fluoride ions, were evaluated using the empirical equation obtained by RSM (equations (14) and (15)).

The pH was one of the significant factor (P > 0.0001) and influenced the biosorption of F⁻ ions by algal biomass. The biosorption of F⁻ ions decreased with pH. The biosorption of F⁻ ions was maximum at pH 2.0. The biomass will have a net positive charge at pH values below the point of zero charge (<5.0 and <6.0 for zirconium-doped C. protothecoides and N. oculata biomass, respectively). The nitrogen-containing functional groups like imidazoles present on the biomass might get protonated at lower pH conditions. It is assumed that increased adsorption at lower pH is because of electrostatic attractions between the fluoride anions and positively charged adsorbent. Another ion responsible for fluoride ion removal is the hydrogen ion that links algal cell wall with the fluoride ion (Gupta et al., 2010). With the increase in pH, there is an increase in the negatively charged sites and this leads to electrostatic repulsion of fluoride ions (Namasivayam and Yamuna, 1995). The initial fluoride concentration also affects the biosorption of F⁻ ions significantly (P > 0.0001). The increase in the biosorption of fluoride ions with the increase in initial fluoride concentration may be due to increase in the driving force for mass transfer to take place. The biosorbent dosage plays a significant role in the biosorption of fluoride ions (P > 0.0001). The increase in the percentage fluoride removal with an increase in the biosorbent dosage may be due to the higher availability of sites for binding with fluoride ions. The interactive effects of two independent variables on the response are displayed in 3D surface plots (online Figure SI-2 for zirconium impregnated C. protothecoides and online Figure SI-3 for zirconium impregnated N. oculata). Online Figures SI-2 and SI-3 show the interactive effect of two variables among initial fluoride concentration (A) 10–100 ppm, adsorbent dose (B) 1–10 g/L, pH (C) 2.0–8.0 and contact time (D) 30–240 min. The predicted versus actual values plot for fluoride adsorption rate onto zirconium-doped C. protothecoides and N. oculata are shown in online Figure SI-4(a) and (b), respectively.

% Biosorptionoffluorideusingzirconium - doped C . protothecoides, R_{1} = 60.5 + 0.32 * initalconcentration + 67.7 * adsorbentdose - 14.5 * pH - 0.04 * contenttime - 0.2 * initialconcentration * adsorbentdose - 6.73 * 10^{*} initialconcentration * pH + 2 * 10^{- 5} * initialconcentration * contacttime - 0.76 * adsorbentdose * pH + 9.35 * 10^{- 3} * adsorbentdose * contacttime + 1.7 * pH * contacttime - 1.15 * 10^{- 3} * {initialconcentration}^{2} - 11.5 * {adsorbentdose}^{2} + 1.2 * {pH}^{2} + 1.73 * 10^{- 4} * contact {time}^{2}

(14)

% Biosorptionoffluorideusingzirconium - doped N . oculata, R_{1} = 58.4 + 0.34 * initialconcentration + 113.8 * adsorbentdose - 20.8 * pH - 0.05 * contacttime - 0.092 * initialconcentration * adsorbentdose - 0.026 * initialconcentration * pH + 1.43 * 10^{- 4} * initialconcentration * contacttime - 2 * adsorbentdose * pH + 7.56 * 10^{- 3} * adsorbentdose * contacttime - 6.48 * 10^{- 3} * pH * contacttime - 2.53 * 10^{- 3} * {initialconcentration}^{2} - 48.7 * {adsorbentdose}^{2} + 2.14 * {pH}^{2} + 2.6 * 10^{- 4} * {contacttime}^{2}

(15)

Mechanism of fluoride removal

Till now, only a few published articles have been successfully explained the sorption mechanism. The algal cell walls generally contain either polysaccharides or a variety of glycoproteins or both. The main reason behind the removal of fluoride was considered to be the protonation of hydrogen ions and the presence of amine groups on the algal biomass. Maximum fluoride removal was at acidic pH due to the protonation of the biosorbent and the speciation of fluoride ions (Guibal et al., 2001). The important mechanisms namely electrostatic attraction, ion exchange and adsorption are considered to influence the fluoride removal. The selective separation of anions is further increased by surface modification of the algal biomass using tetravalent zirconium ions. The zirconium ions bonds strongly with negatively charged groups present on the algal cell wall and also have high affinity towards fluoride ions. The sorption mechanism is effective even at low fluoride concentrations. The FTIR analysis results confirm that the amine groups present on the biomass are the primary adsorption sites. The adsorption mechanism of the biomass was also investigated using XPS and SEM-EDS. The XPS and EDS spectra established the interaction between fluoride and zirconium ions on the zirconium-impregnated biomass. From the initial investigations of XPS, it appears that fluoride ions bind with zirconium and phosphorus groups present on the surface of algal biomass.

Desorption studies

Maximum desorption of fluoride ion (93.4%) was observed during first adsorption–desorption cycle (Figure 10). To understand the reusability of the biosorbents, adsorption–desorption cycle of fluoride was repeated four times using the same biosorbent. It was noticed that the adsorption capacities of the biosorbents reduced by 6.55% during the first regeneration and after that decreases gradually.

Figure 10.

Adsorption/desorption cycles for fluoride removal by zirconium-doped Chlorella protothecoides.

Effect of co-anions on the fluoride adsorption

The presence of co-ions can either increase or decrease the adsorption of anions (Errais, 2011). The adsorption of ions may sometimes not be affected by the addition of ions (Baghriche et al., 2008). Hence, the effect of the presence of co-ions such as sulphate, nitrate, chloride, bicarbonate, and phosphate on the fluoride adsorption phenomena was studied by considering mixtures of 10 and 100 ppm concentrations of salts and 10 ppm initial fluoride concentration. The results of the study of the adsorption in the presence of the co-anions are shown in Figure 11. It was observed that the fluoride adsorption was least affected by chloride and was most affected by the presence of bicarbonates in the solution mixture. This can be interpreted by the fact that the bicarbonate ions compete more with the active sites present on the biomass than the other anions and hence the number of active sites for adsorption gets reduced. A similar result was observed by Aravind and Elango (2006) and Karthikeyan et al. (2004). The chances of binding of the co-ions to the biomass when compared to fluoride depends on several factors such as the chemistry of the ions, pH of the solution, the nature of the binding sites, the amount of binding sites, the diversity of chemical species, fluoride ion concentration and the selectivity of the biomass to bind certain species.

Figure 11.

Effect of co-ions on the fluoride adsorption.

Comparison with other adsorbents

The percentage adsorption of fluoride ions from the aqueous solution by different adsorbents reported earlier in the literature was compared with the values obtained in the present study. Although the operating conditions of the other studies might slightly vary from the present work, the percentage adsorption by zirconium-doped algal biosorbents are in par with that of other adsorbents found in the literature. A comparison of the uptake capacities of different biosorbents are shown in Table 10.

Table 10.

Comparative evaluation of various adsorbents for fluoride removal.

Adsorbents	pH	Concentration range, ppm	Uptake capacity (mg/g)	References
Aluminium hydroxide	4–9	5–30	23.7	Shimelis et al. (2006)
Calcium chloride modified natural zeolite	4–9	25–100	1.766	Zhang et al. (2011)
Zirconium phosphate	2–12	1–10	4.268	Swain et al. (2011)
Fe–Al–Ce nano-adsorbent	42	6.5–7.5	2.77	Chen et al. (2011)
Cellulose@HAP nanocomposites	5–10	4–9	2.76	Yu et al. (2013)
Titanium/chitosan	7	5–40	9	Jagtap et al. (2009)
Zirconium-doped Chlorella protothecoides	2–8	10–100	9.4808	Present study
Zirconium-doped Nannochloropsis oculata	2–8	10–100	8.5465	Present study

Conclusion

The CCD of RSM was used to optimize the various factors including pH, initial fluoride concentration, adsorbent dosage and contact time, which influenced the adsorption of fluoride onto zirconium-doped C. protothecoides and N. oculata biomass. From CCD, it was found that a combination of parameters has a significant effect on the biosorption of fluoride ions. A maximum removal of fluoride ions (92.21%) was observed at pH 2.0, initial fluoride concentration 92 ppm, adsorbent dosage 10 g/L and contact time 240 min using zirconium-doped C. protothecoides. Whereas, zirconium-doped N. oculata removed 90.4% fluoride ions at pH 2.0, the initial fluoride concentration of 79 ppm, adsorbent dosage 9.77 g/L and contact time 240 min, respectively. RSM was very useful in determining the significant model and numerical evaluation of the biosorption process. However, the suitability of these biosorbents for plant scale studies must be determined. Regeneration studies are also needed to enhance the economic feasibility of the process.

Footnotes

Acknowledgements

The authors are thankful to the Principal and Management of Siddaganga Institute of Technology, Tumkur, Karnataka, India for their constant support and encouragement. The authors would also like to thank Dr Madhu Chennabasappa, Department of Physics, Siddaganga Institute of Technology, Tumkur for his help in interpreting the XPS spectra.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research work is partially supported by R&D grant from The Institution of Engineers (India).

Supplemental Material

The online supplementary material is available at .

References

Agarwal

Rai

Shrivastav

(2003) Defluoridation of water using amended clay. Journal of Cleaner Production 11: 439.

Amin

Talpur

Balouch

(2015) Biosorption of fluoride from aqueous solution by white – Rot fungus Pleurotus eryngii ATCC 90888. Environmental Nanotechnology, Monitoring & Management 3: 30.

Aravind

Elango

(2006) Adsorption of fluoride onto magnesia-equilibrium and thermodynamic study. Indian Journal of Chemical Technology 13: 476.

Arteaga

Li-chan

(1994) Systematic experimental design for product formula optimization. Trends in Food Science & Technology 5: 243.

Ayoob

Gupta

(2006) Fluoride in drinking water: A review on the status and tress effects. Environmental Science & Technology 36: 433.

Baghriche

Djebbar

Sehili

(2008) Kinetic study on adsorption of cationic dye (Methyl Green) on activated carbon from aqueous medium. Science Technology 27: 57.

Bhatnagar

Kumar

Sillanpää

(2011) Fluoride removal from water by adsorption – A review. Chemical Engineering Journal 171(3): 811.

Biswas

Inoue

(2008) Adsorptive removal of As(V) and As(III) from water by a Zr(IV)-loaded orange waste gel. Journal of Hazardous Materials 154: 1066.

Chen

Wang

(2011) Optimization of a Fe–Al–Ce nano-adsorbent granulation process that used spray coating in a fluidized bed for fluoride removal from drinking water. Powder Technology 206: 291.

10.

Chhipa

Acharya

Bhatnagar

(2013) Determination of sorption potential of fermentation industry waste for fluoride removal. International Journal of Bioassays 2(3): 568.

11.

Das

Parida

(2003) Physicochemical characterization and adsorption behavior of calcined Zn/Al hydrotalcite-like compound (HTlc) towards removal of fluoride from aqueous solution. Journal of Colloid and Interface Science 261(2): 213.

12.

Dou

Mohan

Pittman

(2012) Remediating fluoride from water using hydrous zirconium oxide. Chemical Engineering Journal 198: 236.

13.

Errais E (2011) Reactivity of the natural clays area study of the adsorption of anionic dyes. PhD Thesis, Strasbourg University, Strasbourg, France.

14.

Evans

Milligan

Montgomery

(1987) Collagen cross-linking: new binding sites for mineral tannage. Journal of American Leather Chemists Association 82(4): 86.

15.

Ghosh

Chakrabarti

Biswas

(2014) Agglomerated nanoparticles of hydrous Ce(IV) + Zr(IV) mixed oxide: Preparation, characterization and physicochemical aspects on fluoride adsorption. Applied Surface Science 307: 665.

16.

Goldberg

Johnston

(2001) Mechanisms of arsenic adsorption on amorphous oxides evaluated using macroscopic measurements, vibrational spectroscopy, and surface complexation modelling. Journal of Colloid and Interface Science 234: 204.

17.

Guibal

Ruiz

Vincent

(2001) Platinum and palladium sorption on chitosan derivatives. Separation Science and Technology 36: 1017.

18.

Gupta

Rastogi

Nayak

(2010) Biosorption of nickel onto treated alga (Oedogonium hatei): Application of isotherm and kinetic models. Journal of Colloid and Interface Science 342: 533.

19.

Jagtap

Thakre

Wanjari

(2009) New modified chitosan-based adsorbent for defluoridation of water. Journal of Colloid and Interface Science 332: 280.

20.

Karthikeyan

Shunmugasundarraj

Meenakshi

(2004) Adsorption dynamics and the effect of temperature of fluoride at alumina-solution interface. Journal of Indian Chemical Society 81: 461.

21.

Kristo

Biliaderis

Tzanetakis

(2003) Modelling of the acidification process and rheological properties of milk fermented with a yogurt starter culture using response surface methodology. Food Chemistry 83: 437.

22.

Maiti

Basu

(2011) Chemical treated laterite as promising fluoride adsorbent for aqueous system and kinetic modelling. Desalination 265: 28.

23.

Mekonen

Kumar

(2001) Integrated biological and physiochemical treatment process for nitrate and fluoride removal. Water Research 35: 3127.

24.

Myers

Montgomery

(2002) Response Surface Methodology, 2nd edn. New York: Wiley.

25.

Namasivayam

Yamuna

(1995) Adsorption of direct red 12 B by bio gas residual slurry: equilibrium and rate processes. Environmental Pollution 89: 1.

26.

Rajan

Alagumuthu

(2013) Study of fluoride affinity by zirconium impregnated walnut shell carbon in aqueous phase: Kinetic and isotherm evaluation. Journal of Chemistry 2013: 235048.

27.

Sarkar

Mohapatra

Ray

(2007) Synthesis and characterization of sol–gel derived ZrO2 doped Al2O3 nanopowder. Ceramics International 33: 1275.

28.

Shimelis

Zewge

Chandravanshi

(2006) Removal of excess fluoride from water by aluminum hydroxide. Bulletin of the Chemical Society of Ethiopia 20: 17.

29.

Smith

(1998) Infrared Spectral Interpretation: A Systematic Approach, 1st ed. London: CRC Press.

30.

Sujana

Soma

Vasumathi

(2009) Studies on fluoride adsorption capacities of amorphous Fe/Al mixed hydroxides from aqueous solutions. Journal of Fluorine Chemistry 130: 749.

31.

Swain

Patnaik

Singha

(2011) Kinetics, equilibrium and thermodynamic aspects of removal of fluoride from drinking water using meso-structured zirconium phosphate. Chemical Engineering Journal 171: 1218.

32.

Tan

Ahmad

Hameed

(2008) Adsorption of basic dye on high-surface-area activated carbon prepared from coconut husk: Equilibrium, kinetic and thermodynamic studies. Journal of Hazardous Materials 154: 337.

33.

Tang

Zhang

(2016) Efficient removal of fluoride by hierarchical Ce–Fe bimetal oxides adsorbent: Thermodynamics, kinetics and mechanism. Chemical Engineering Journal 283: 721.

34.

Tanuma

Okamoto

Ohnishi

(2009) Activated zirconium oxide catalysts to synthesize dichloropentafluoropropane by the reaction of dichlorofluoromethane with tetrafluoroethylene. Applied Catalysis A 359: 158.

35.

Vasconcelos

AFD

Barbosa

Dekker

RFH

(2000) Optimization of laccase production by Botryosphaeria sp. in the presence of veratryl alcohol by the response-surface method. Process Biochemistry 35: 1131.

36.

Viswanathan

Meenakshi

(2008) Effect of metal ion loaded in a resin towards fluoride retention. Journal of Fluorine Chemistry 129: 645.

37.

Viswanathan

Sundaram

Meenakshi

(2009) Removal of fluoride from aqueous solution using protonated chitosan beads. Journal of Hazardous Materials 161: 423.

38.

Wolter

Piascik

Stoner

(2011) Characterization of plasma fluorinated zirconia for dental applications by X-ray photoelectron spectroscopy. Applied Surface Science 257: 10177.

39.

Xiang

Zhang

(2014) Synthesis and characterization of cotton-like Ca–Al–La composite as an adsorbent for fluoride removal. Chemical Engineering Journal 250: 423.

40.

Tong

(2013) Removal of fluoride from drinking water by cellulose@hydroxyapatite nanocomposites. Carbohydrate Polymers 92: 269.

41.

Zhang

(2012) A low-cost and high efficient zirconium-modified-Na-attapulgite adsorbent for fluoride removal from aqueous solutions. Chemical Engineering Journal 183: 315.

42.

Zhang

Fan

Wang

(1995) Effect of oxide dopant on the structure of fluorozirconate glasses studied by X-ray photoelectron spectroscopy. Journal of Materials Science 30: 1445.

43.

Zhang Y, Yang M, Dou XM, et al. (2005) Arsenate adsorption on an Fe-Ce bimetal oxide adsorbent: Role of surface properties. Environmental Science & Technology 39: 7246.

44.

Zhang

Tan

Zhong

(2011) Defluorination of wastewater by calcium chloride modified natural zeolite. Desalination 276: 246.

Modelling of fluoride sorption from aqueous solution using green algae impregnated with zirconium by response surface methodology

Abstract

Keywords

Introduction

Experimental procedure

Materials

Analysis of fluoride ion concentration

Procurement and culturing of algal strains

Cultivation of algae and preparation of the algal powder

Preparation of algal biosorbents by zirconium impregnation

Characterization of zirconium-impregnated algal biomass

Zeta potential measurement

Batch biosorption experiments

Experimental design and data analysis

Equilibrium studies

Adsorption kinetics

Desorption studies

Results and Discussion

Characterization of the biosorbents

Equilibrium studies

Adsorption kinetics

Statistical analysis

Validation of response surface models and statistical analysis

Optimization of variables for the removal of fluoride ions

Mechanism of fluoride removal

Desorption studies

Effect of co-anions on the fluoride adsorption

Comparison with other adsorbents

Conclusion

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

Supplemental Material

References