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fluoride and arsenic removal from aqueous solutions (Viswanathan and ... The instrument was calibrated using fluoride standards provided by Mettler Toledo to .... by interpolating the zeta potential data to the zero potential (Dou et al., 2012).
Research Article

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

Adsorption Science & Technology 2017, Vol. 35(1–2) 194–217 ! The Author(s) 2016 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0263617416674014 journals.sagepub.com/home/adt

Poornima G Hiremath and Thomas Theodore Department of Chemical Engineering, Siddaganga Institute of Technology, India

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 zirconiumdoped 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 zirconiumdoped 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, Chlorella protothecoides, Nannochloropsis oculata, Zirconium, CCD Submission date: 11 May 2016; Acceptance date: 22 September 2016

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 Corresponding author: Thomas Theodore, Department of Chemical Engineering, Siddaganga Institute of Technology, B.H. Road, Tumkur-572 103, Karnataka, India. Email: [email protected]

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(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, coalfired 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 Ca2þ 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 (FTIR) 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 (perfectIONTM 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.

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Adsorption Science & Technology 35(1–2) Table 1. Composition of Bold’s basal medium. Component

mg/L

Component

mg/L

Co(NO3)2.6H2O CuSO4.5H2O MnCl2.4H2O H3BO3 ZnSO4.7H2O

4.9 15.7 14.2 114.2 88.2

EDTA KH2PO4 FeSO4.7H2O MgSO4.7H2O KNO3

500 1250 49.8 1000 1250

Table 2. Composition of Bold’s basal medium (modified). Component

Stock solution (mL)

mL/L

Component

Stock solution

mL/L

KH2PO4 CaCl2.2H2O MgSO4.7H2O NaNO3 NaCl

8.75 g/500 1.25 g/500 3.75 g/500 12.5/500 1.25/500

10 10 10 10 10

Na2EDTA.2H2O KOH FeSO4.7H2O H2SO4 Trace metal solution

10 g/L 6.2 g/L 4.98 g/L — —

1 1 1 1 1

Table 3. Composition of trace metal solution. Component

Quantity (g/L)

Component

Quantity (g/L)

H3BO3 MnCl2.4H2O ZnSO4.7H2O

2.86 1.81 0.222

Na2MoO4.2H2O CuSO4.5H2O Co(NO3)2.6H2O

0.390 0.079 0.0494

Procurement and culturing of algal strains Chlorella protothecoides (SAG 211-10C), and Nannochloropsis oculata (SAG 38.85) were procured from EPSAG, Go¨ttingen, 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.

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 C. 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

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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 C 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 C. The zirconium-doped algae were then filtered, washed with water to remove free zirconium ions, dried in an oven at 100 C 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 TwoTM 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 Ka 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 (z) 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 C. The initial pH was adjusted using HNO3 or NaOH. Biosorbent suspensions with or without F at the preferred pH were shaken at 25 C 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 C in 250-mL polypropylene flasks. The initial pH of the samples was maintained using

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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 ¼

ðC0  Ce Þ  100 C0

ð1Þ

where C0 and Ce (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 a 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 þ1. 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 ¼ 0 þ

k X

i xi þ

i

k X

ii x2i þ

ii

k X

ij xi xj

ð2Þ

i5j

Table 4. The experimental factors and levels for the CCD. Factor

Low level (1)

Medium level (0)

High level (þ1)

Initial F concentration, ppm Biosorbent dosage, g/L pH Contact time, min

10 1 2 30

55 5.5 5 135

100 10 8 240

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where Y is the response; 0 is a constant, i are linear coefficients, ij are second-order interaction terms and jj are quadratic coefficients; the variables xi and xj are non-coded independent variables. Since four variables are involved in the present study, k ¼ 4. 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 C 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 C  2 C). 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. 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

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Adsorption Science & Technology 35(1–2) Table 5. Particle size of the algal biosorbents. Biosorbent

D (v, 0.1), mm

D (v, 0.5), mm

D (v, 0.9), mm

Chlorella protothecoides-Zr Nannochloropsis oculata-Zr

0.26 0.68

2.50 15.32

6.98 162.15

Table 6. BET surface area and specific gravity. Biosorbent

BET surface area (m2/g)

Specific gravity

Chlorella protothecoides-Zr Nannochloropsis oculata -Zr

16.05 31.91

2.389 1.765

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.

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(a)

(b) Element OK Mg K PK Cl K KK Ca K Fe K Zr L Total

Weight% 29.05 2.04 18.06 3.37 3.40 15.51 20.32 8.23 100.00

Element CK OK FK Mg K PK KK Ca K Fe K Zr L Total

Atomic% 51.79 2.40 16.63 2.71 2.48 11.04 10.38 2.57

Weight% 14.83 40.49 2.01 1.46 12.99 1.28 7.32 13.72 5.90 100.00

Atomic% 25.21 53.23 1.06 1.23 8.57 0.67 3.73 5.02 1.32

Figure 2. EDS of Chlorella protothecoides þ Zr biosorbent (a) before and (b) after fluoride adsorption.

(a)

(b) Element CK OK Mg K PK Cl K Ca K Zr L Total

Weight% 20.12 39.37 1.91 7.50 3.15 15.27 12.68 100.00

Atomic% 33.07 48.58 1.55 4.78 1.76 7.52 2.74

Element CK OK Mg K Si K Ca K Zr L Total

Weight% 25.52 46.60 2.85 3.20 12.09 9.75 100.00

Atomic% 37.42 51.31 2.06 2.01 5.31 1.88

Figure 3. EDS of Nannochloropsis oculata þ Zr biosorbent (a) before and (b) after fluoride adsorption.

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. 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 cm1 that is attributed to the overlapping of –NH2 and – OH stretching vibrations (Viswanathan et al., 2009); at 2850 cm1 due to C–H stretching (Amin et al., 2015); and at 1724 and 1654 cm1 due to the involvement of C ¼ C or C ¼ O

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(a) 75

(b) Chlorella protothecoides Before adsorption

Nannochloropsis Before adsorption Nannochloropsis After adsorption

Chlorella protothecoides After adsorption 85

70

80

65

75 %T

60 70

55 65 50

45 4000 3500 3000 2500 2000 1500 1000 cm–1

60

500

0

55 4500

4000 3500

3000

2500 2000

1500

1000

500

cm–1

Figure 4. FTIR spectra of zirconium-doped (a) Chlorella protothecoides and (b) Nannochloropsis oculata biosorbent before and after adsorption.

groups. The peaks at 1558 and 1457 cm1 may be attributable to N–H bending in the amine group and at 1033 cm1 due to C–O stretching of carboxylic acids. The peaks at 525 cm1 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. 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 Zr3d3/2, Zr3d5/2 and P2s were located at 187, 184.52 and 191.2 eV, respectively. After fluoride adsorption, the Zr3d3/2, Zr3d5/2 and P2s peaks broadened and a shoulder appeared for these peaks, on careful observation these can be assigned to metalfluorine 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). 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

50

100

150

O KLL

P 2p 0

200

F 1s Fe 2p

O loss

K 2p C loss Ca 2p

Intensity (cps)

C 1s

(a)

Zr3d P 2s

203

O 1s

Hiremath and Theodore

CP before adsorption CP after adsorption

0

50

100

150

1050

1200

O KLL

Zr3d P 2s

900

200

K 2p C loss Ca 2p

O loss

Intensity (cps)

C 1s

(b)

450 600 750 Binding Energy (eV) P 2p

300

F 1s Fe 2p

150

O 1s

0

NO before adsorption NO after adsorption

0

150

300

450 600 750 Binding Energy (eV)

900

1050

1200

Figure 5. XPS plot of (a) Chlorella protothecoides (CP) þ Zirconium before and after adsorption at pH 2, (b) Nannochloropsis oculata (NO) þ Zirconium before and after adsorption at pH 2.

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(Zr3d3/2 doublet for Zr-F)/ A(Zr3d3/2 doublet for Zr-O) ¼ A(Zr3d5/2 doublet for Zr-F)/A(Zr3d5/2 doublet for Zr-O) (where A means peak area). 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.

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Adsorption Science & Technology 35(1–2)

Figure 6. Deconvoluted XPS spectra of Zr3d and P2s for (a) Chlorella protothecoides (CP) þ Zirconium before and (b) after adsorption at pH 2, (c) Nannochloropsis oculata (NO) þ 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

BEa (eV)

Area

FWHMb (eV)

%GL

Chlorella protothecoides (CP) þ Zirconium

Before adsorption

Zr-O Zr-O P-O Zr-O Zr-F Zr-O-F P-O P-F Zr-O Zr-O P-O Zr-O Zr-F P-O P-F

3d5/2 3d3/2 2s 3d5/2 3d5/2 3d3/2 2s 2s 3d5/2 3d3/2 2s 3d5/2 3d5/2 2s 2s

184.52 187.00 191.20 183.77 185.99 188.51 191.00 193.43 183.29 185.87 191.75 183.61 186.66 190.47 193.00

2074.02 529.495 5696.73 3841.47 1295.01 1003.99 5673.35 1464.21 5859.72 2487.13 2074.88 8034.80 2050.97 1586.79 1001.26

2.23 1.49 3.77 3.40 2.22 1.95 2.81 2.28 3.54 3.38 3.69 3.50 3.10 2.78 2.69

19 12 35 20 14 15 21 20 20 20 10 27 0 13 19

After adsorption

Nannochloropsis oculata (NO) þ Zirconium

Before adsorption

After adsorption

a

Binding energy (BE). The full width at half maximum (FWHM). c Gaussian:Lorentzian. b

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(a) 0 ppm 100 ppm

10 5

Zeta potential (mV)

0 2

4

-5

6

8

10

pH

-10 -15 -20 -25 -30

(b)

0 ppm 100 ppm

20

Zeta potential (mV)

10

0 2

4

6 pH

8

10

-10

-20

-30

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 Fsolution; adsorbent dose, 10 g/L; at 25 C.

Equilibrium studies The equilibrium concentrations in the solution (Ce) were determined after the system reached equilibrium while the concentrations in the adsorbent (qe) were calculated as in equation (3). qe ¼

ðCo  CeÞV M

ð3Þ

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Adsorption Science & Technology 35(1–2)

(a)

(b) CP NO

0.16

4.5

CP NO

4.0

0.14 3.5 ln qe

1/qe

0.12 0.10 0.08

3.0 2.5

0.06 2.0

0.04

1.5

0.02 0.04

0.08

0.12

0.16

1.5

0.20

2.0

1/Ce

3.0

3.5

4.0

4.5

ln Ce

(c)

(d) 3.6 CP NO

3.4

2.5

40 CP NO

35

3.2 30 25

2.8 qe

ln qe

3.0

2.6

20

2.4 15

2.2 2.0

10

1.8 5 0

100000 ε2

1.5

200000

2.0

2.5 3.0 ln Ce

3.5

4.0

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.

qe is the amount of fluoride adsorbed per unit weight of the sorbent at equilibrium (mg/g), Ce 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). 1

qe ¼ Kf Ce ðnÞ

ð4Þ

Kf and 1/n are Freundlich constants and are measures of adsorption capacity (mg/g) and adsorption intensity or surface heterogeneity. The Freundlich isotherm constants Kf and n were calculated from the slope and the intercept of the plot of ln qe versus ln Ce (Figure 8) and were presented in Table 8. The surface heterogeneity is higher for zirconium-doped

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Table 8. Adsorption isotherm constants of Freundlich, Langmuir, D–R and Temkin models for zirconium-doped Chlorella protothecoides and Nannochloropsis oculata biomass. Zirconium-doped algal biosorbents Isotherm Freundlich isotherm

Langmuir isotherm

D-R isotherm

Parameters (constants)

Chlorella protothecoides-Zr

Nannochloropsis oculata-Zr

Slope:1/n Intercept: ln Kf n Kf (mg/g) R2 Slope: 1/qmKL Intercept: 1/qm qm (mg/g) Ka (L/mg) RL R2 Slope: BD Intercept: ln (qs) qs (mg/g) BD (mol2/kJ2) ED (kJ/mol) R2

0.7176 0.7067 1.3935 2.0272 0.9765 0.443 0.031 31.53 13.973 0.000715 0.718 5E-06 3.1114 22.45 5E-06 310.08 0.3571

0.9378 0.3736 1.0663 0.6882 0.9227 1.4815 0.0114 87.37 129.43 7.73E-05 0.9567 4E-05 3.2155 24.91 4E-05 111.8 0.7198

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). The Langmuir adsorption isotherm model is given by equation (5) (Tan et al., 2008). qe ¼

qm Ka Ce 1 þ Ka Ce

ð5Þ

The adsorbent uptake capacity (qm) is the amount of adsorbate present to form a monolayer (mg/g), and Ka (L/mg) is the Langmuir constant that relates to the energy of adsorption. The Ka and qm values were calculated from the slope and the intercept of the plot 1/qe versus 1/Ce (Figure 8 and Table 8). The values of the Langmuir separation factor, RL obtained from equation (6), indicates whether the adsorption is either unfavourable when RL > 1, linear when RL ¼ 1, favourable when 0 < RL < 1 or irreversible if RL ¼ 0. RL ¼

1 1 þ Ka C0

ð6Þ

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

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sorption capacity at the expense of minimum energy was evident from the values of qm and RL values of 87.37 mg/g and 129.43 L/mg. Since the RL 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 qe ¼ ln qm  "2

ð7Þ

where " is the Polanyi potential, and is equal to RT ln(1 þ 1/Ce),  is the constant related to adsorption energy, R is universal gas constant, and T is the temperature in Kelvin. D-R isotherm constants  and qm were calculated from the slope and intercept of the plot ln qe versus "2 (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). pffiffiffiffiffiffiffiffiffi E ¼ 1= 2 ð8Þ 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). qe ¼ AT þ BT lnCe

ð9Þ

AT and BT are the Temkin isotherm equilibrium binding constant (L/g) and Temkin isotherm constant. The constants AT and BT were determined from the slope and intercept of the plot qe against ln Ce (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). 1 1 k1 ¼ þ qt q1 q1 t

ð10Þ

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Here, qt 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, min1. 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). t 1 t ¼ þ qt k2 q22 q2

ð11Þ

where q2 is the maximum sorption capacity, mg/g; qt 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/qt 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). qt ¼ lnðÞ þ lnt

ð12Þ

where,  is the sorption rate (mmol/(g min)) and  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). qt ¼ ki t1=2 þ C

ð13Þ

where ki 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.

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.

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(a)

(b)

0.16

40 CP NO

0.15

CP NO

35 30

0.14 t/qe

1/qe

25 0.13

20 15

0.12

10 0.11 5 0.10 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 1/t

(c)

9.0

8.5

8.5

8.0

7.5

7.0

7.0

6.5

6.5 6

8

10 t^0.5

12

150

200

14

250

CP NO

8.0

7.5

4

100

9.5

9.0

2

50

t

qe

qe

0

(d)

CP NO

9.5

0

2.5

16

3.0

3.5

4.0 ln t

4.5

5.0

5.5

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) Constant, q2 (mg/g) R2

0.0420 9.4808 0.9992

0.03 8.5465 0.9979

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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 secondorder 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 ¼ 0.9790 and the adjusted R2 ¼ 0.9933 for zirconium-doped C. protothecoides; the predicted R2 ¼ 0.9734 and the adjusted R2 ¼ 0.9914 for zirconiumdoped 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 ( 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

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online Figure SI-4(a) and (b), respectively. %Biosorption of fluoride using zirconium  doped C:protothecoides; R1 ¼ 60:5 þ 0:32  inital concentration þ 67:7  adsorbent dose  14:5  pH  0:04  content time  0:2  initial concentration  adsorbent dose6:73  103  initial concentration  pHþ 2 105  initial concentration  contact time  0:76  adsorbent dose  pH þ 9:35  103  adsorbent dose  contact time þ 1:7  pH  contact time  1:15  103  initial concentration2  11:5  adsorbent dose2 þ 1:2  pH2 þ1:73  104  contact time2 ð14Þ %Biosorption of fluoride using zirconium  doped N:oculata, R1 ¼ 58:4 þ 0:34  initial concentration þ 113:8  adsorbent dose  20:8  pH  0:05  contact time  0:092  initial concentration  adsorbent dose  0:026  initial concentration  pH þ 1:43  104  initial concentration  contact time  2  adsorbent dose  pH þ 7:56  103  adsorbent dose  contact time  6:48  103  pH  contact time  2:53  103  initial concentration2  48:7  adsorbent dose2 þ 2:14  pH2 þ 2:6  104  contact time2 ð15Þ

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 zirconiumimpregnated 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

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100 % Fluoride removal 80

60

40

20

0 Fresh

I

II

III

IV

Reuse cycles

Figure 10. Adsorption/desorption cycles for fluoride removal by zirconium-doped Chlorella protothecoides.

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.

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.

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

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% Fluoride Removal At 10 ppm co-ion concn

100 90

% Fluoride Removal At 100 ppm co-ion concn

80 70 60 50 40 30 20 10 0 NaCl

NaNO3 Na2SO4 NaHCO3 Co-anions

Figure 11. Effect of co-ions on the fluoride adsorption. Table 10. Comparative evaluation of various adsorbents for fluoride removal. Adsorbents

pH

Concentration range, ppm

Uptake capacity (mg/g)

References

Aluminium hydroxide Calcium chloride modified natural zeolite Zirconium phosphate Fe–Al–Ce nano-adsorbent Cellulose@HAP nanocomposites Titanium/chitosan Zirconium-doped Chlorella protothecoides Zirconium-doped Nannochloropsis oculata

4–9 4–9

5–30 25–100

23.7 1.766

Shimelis et al. (2006) Zhang et al. (2011)

2–12 42 5–10 7 2–8 2–8

1–10 6.5–7.5 4–9 5–40 10–100 10–100

4.268 2.77 2.76 9 9.4808 8.5465

Swain et al. (2011) Chen et al. (2011) Yu et al. (2013) Jagtap et al. (2009) Present study Present study

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.

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

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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. 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 http://journals.sagepub.com/doi/suppl/10.1177/ 0263617416674014.

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