Simultaneous removal of binary mixture of Brilliant Green and Crystal Violet using derivative spectrophotometric determination, multivariate optimization and adsorption characterization of dyes on surfactant modified nano-γ-alumina

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 137 (2015) 1016–1028

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Simultaneous removal of binary mixture of Brilliant Green and Crystal Violet using derivative spectrophotometric determination, multivariate optimization and adsorption characterization of dyes on surfactant modified nano-c-alumina Javad Zolgharnein ⇑, Maryam Bagtash, Tahere Shariatmanesh Department of Chemistry, Faculty of Science, Arak University, Arak 38156-8-8394, Iran

h i g h l i g h t s

g r a p h i c a l a b s t r a c t

 Simultaneous removing of Brilliant

Green and Crystal Violet from aqueous solution.  Introducing a new efficient adsorbent called surfactant-modified nano calumina.  A first-order derivative spectrophotometry used for simultaneous dyes assay.  Developing Box–Behnken design for multivariate optimization of binary adsorption.  FT-IR analysis, isotherms modeling and kinetic studies of the binary adsorption.

a r t i c l e

i n f o

Article history: Received 9 June 2014 Received in revised form 7 August 2014 Accepted 24 August 2014 Available online 8 September 2014 Keywords: Brilliant Green Box–Behnken design Crystal Violet Derivative spectrophotometry Surfactant-modified nano-alumina

a b s t r a c t The present study deals with the simultaneous removal of Brilliant Green (BG) and Crystal Violet (CV) by surfactant-modified alumina. The utilization of alumina nanoparticles with an anionic surfactant (sodium dodecyl sulfate (SDS)) as a novel and efficient adsorbent is successfully carried out to remove two cationic dyes from aqueous solutions in binary batch systems. A first-order derivative spectrophotometric method is developed for the simultaneous determination of BG and CV in binary solutions. The linear concentration range and limits of detection for the simultaneous determination of BG and CV were found to be: 1–20, 1–15 mg/L, 0.3 and 0.5 mg/L, respectively. The influence of various parameters, such as contact time, initial concentration of dyes and sorbent mass on the dye adsorption is investigated. A response surface methodology achieved through performing the Box–Behnken design is utilized to optimize the removal of dyes by surfactant-modified nanoparticle alumina through a batch adsorption process. The proposed quadratic model resulting from the Box–Behnken design approach fitted very well with the experimental data. The optimal conditions for dye removal were contact time t = 50 min, sorbent dose = 0.036 g, CBG (Initial BG concentration) = 215 mg/L and CCV (Initial CV concentration) = 170 mg/L. Furthermore, FT-IR analysis, the isotherms and kinetics of adsorption were also explored. Ó 2014 Elsevier B.V. All rights reserved.

⇑ Corresponding author. Tel.: +98 863 4173401; fax: +98 863 4173406. E-mail addresses: [email protected], [email protected] (J. Zolgharnein). http://dx.doi.org/10.1016/j.saa.2014.08.115 1386-1425/Ó 2014 Elsevier B.V. All rights reserved.

J. Zolgharnein et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 137 (2015) 1016–1028

Introduction Discharge of synthetic dyes has been one of the more important categories of aquatic pollutants in recent years [1–3]. Due to various commercial usages, the impact and toxicity of dyes that are released into the environment have been extensively studied [1–3]. Brilliant Green (BG) and Crystal Violet (CV) are among the cationic dyes (Fig. 1) which have been shown to have harmful effects on human beings [4]. Brilliant Green dye is used for various purposes, such as in the production of cover paper in the paper industry; for biological staining; as a dermatological agent; in veterinary medicine; and also as an additive, to inhibit the propagation of mold, intestinal parasites and fungus [5]. In humans, Brilliant Green causes irritation to the gastrointestinal tract and the respiratory tract. Skin contact causes irritation, with redness and pain [4,5]. Crystal Violet is a well-known dye used for dyeing cotton, silk, paper, bamboo, weed, straw and leather [6,7]. It is a mutagen and mitotic poison; its decomposition releases toxic substances, such as CO, CO2 and NO [6,8]. However, the complex aromatic molecular structures of dyes make them more stable and more difficult to biodegrade. These dyes’ hazardous effluents need to be treated before being discharged into the environment [1–8]. Many treatment methods, including chemical oxidation, adsorption, coagulation/flocculation, biological treatment, membrane separation and ion exchange, are available for the removal of dyes from industrial effluents [1–11]. Among these methods, adsorption has evolved into one of the most effective physical processes for the treatment of wastewaters, since it can produce high-quality water and also be a process that is economically feasible [1–11]. Therefore, it is desirable to obtain alternative adsorbents with high adsorptive capacities and low production costs [1,2,7,8,9]. In recent years, nano-sized metal oxides, including nano-sized Fe2O3, Fe3O4, TiO2, SiO2, Al2O3, MgO and CeO2, have delivered some promising applications as adsorbents in water treatment [9,10,12–14] due to their large surface areas and high activity levels, caused by the size quantization effect [13–15]. Among this category of

Fig. 1. Chemical structure of (A) BG and (B) CV.

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nano-sized adsorbents, metal oxides, such as alumina, silica and titanium dioxide, having high surface areas, are hydrophilic in nature, and show limited adsorption affinities for hydrophobic compounds [14–16]. Treatment of this kind of nano-sized metal oxide by surfactants can enhance their adsorptive tendency toward organic pollutants [14–17]. Surfactants that have been used for this purpose have either anionic or cationic head groups, with long chain hydrocarbon molecules forming the surfactant tail [16,17]. The pH point of a zero charge of nano-sized sorbent characterizes the type of behavior of modifiers, such as surfactants with sorbents [17]. The adsorption of a surfactant on this kind of metal oxide nano-sorbent, such as an alumina oxide surface, occurs due to electrostatic attractions between anionic head groups of surfactants and positively charged adsorbent surfaces [16]. Interaction between the hydrocarbon chains of surfactants leads to the formation of admicelles and hemimicelles on the surface of the sorbent [17–21]. The structure of hemimicelles is such that the surfactant aggregate presents a hydrocarbon surface to the aqueous phase, whereas that of admicelles presents a hydrophilic surface with a hydrocarbon interior [18–21]. This hydrocarbon phase enhances the affinity of the sorbent toward other organic molecules. As a result, surfactant-modified mineral oxides are recognized as extremely versatile adsorbents for organic compounds in a wide polarity range [16–19,21–25]. Due to the hydrophobic interactions of micelles formed on the surface of nano-sized metal oxides, such as alumina, the organic contaminants escape from the aqueous phase and become concentrated in the microscopic hydrophobic phase. Since the ability of surfactant modified mineral oxides to remove pollutants is strongly influenced by some parameters, such as pH, time, temperature, initial concentration of pollutants and sorbent mass [15,16], it is therefore essential to design an appropriate process for maximizing the removal efficiency of pollutants by adsorbents [15,16]. Due to several advantages of multivariate optimization, especially its experimental design, compared to the conventional ‘‘one-at-a-time’’ method, has been attracting interest in relation to the adsorption process [3,15,16,25–28]. It provides more information with fewer experiments, running a response surface methodology (RSM) which leads to an empirical mathematical model which relates the removal percent (R %) of sorbate with effective variables and their interactions [26–28]. Up to now, most removal dyes studies have focused on single dyes and the use of two or more mixtures of dye is rare [1–8,29–32]. The most heavily discharging industrial effluents include a mixture of several dyes, so it is more important and necessary to study the simultaneous removal of two or more dyes from aqueous solutions which are more likely to be found in real effluent samples [28–32]. Many problems could be solved through multi-component adsorption: an important problem that is encountered regularly is the simultaneous analysis of dyes in solution. Due to some advantages of the UV–Vis spectrophotometric method, such as economic efficiency, simplicity, facility, accuracy and reproducibility, compared to other analytical techniques, it is used usually for dye assessments [29– 31]. However, the simultaneous determination of dyes by the use of a traditional spectrophotometric method is difficult because of a serious overlapping of their absorption spectra. The derivative techniques and multivariate calibration methods, such as Principal Component Regression (PCR) and Partial Least Squares (PLS) regression, play a very important role in overcoming the problem of pre-treatments in the multi-component analysis by UV–Vis spectrophotometry [30,31]. Therefore, the derivative spectrophotometric has been used, due to its facile and simple approach compared to other chemometric methods for simultaneous determination of dyes in mixtures [30–35]. However, we have applied an experimental design approach for the optimization of simultaneous adsorption of Brilliant Green and Crystal Violet onto recent surfactant modified nano-c-alumina

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(SMA). Derivative spectrophotometry has been applied for the simultaneous monitoring of dye concentrations for evaluating the removal percent (R %) and uptake capacity (q) of the adsorption process. Effects of operational parameters, such as adsorbent dosage, initial dye concentration and contact time, were investigated, in order to evaluate the removal percent (R %) of dyes by SMA in binary systems. A Box–Behnken design is used as a response surface methodology approach to find effective adsorption factors, leading to optimum conditions for maximum dye removal from aqueous solutions by surfactant modified nano-c-alumina (SMA) being obtained [25–28,36–38]. It also demonstrates the potential of SMA as a new efficient adsorbent for the removal of dyes in binary systems from aqueous waste. The sorbent–sorbate equilibrium behavior and kinetics of the adsorption process is also investigated.

sodium dodecyl sulfate (SDS). All were purchased from Merck (Darmstadt, Germany). All reagents used in this study were of an analytical grade. Preparation of surfactant-modified nano-alumina A 200 g sample of alumina nanoparticles (NPs) were shaken for 24 h with 2 L of SDS solution having a 20,000 mg/L concentration. The NaCl dose was chosen as 2500 mg/L and the pH of solution sets at 4.4 ± 0.1. After shaking, the supernatant was discarded and the NPs of alumina were washed thoroughly with distilled water and dried at 60 °C for 24 h. The NPs of alumina thus obtained are called surfactant-modified NPs alumina and were used as an adsorbent for dye removal [39–41]. Batch procedure

Materials and methods The powders of Brilliant Green (kmax BG = 588 nm) and Crystal Violet (kmax CV = 626 nm) were used (Fig. 1). Nano-c-Al2O3 was obtained from the TECNAN Company. The X-ray diffraction (XRD) spectrum, TEM micrograph and scanning electron microscopy (SEM) (Hitachi S4160) of nano-c-Al2O3 are shown in Figs. 2 and 3, respectively. Surface area and porosity were defined by N2 adsorption–desorption porosimetry using a Tristar II 3020 multisample specific surface analyser. The cationic surfactant was cetyltrimethylammonium bromide (CTAB) and the anionic surfactant

The stock solutions of BG and CV were prepared in the same concentration of 2.07 mmol/L, (1000 mg/L) and (845.3 mg/L), respectively. The test solutions were prepared by diluting and/or mixing desired combinations of BG and CV stock solutions. In a typical run, 10 mL of fresh dye solution was used, and a known amount of dried sorbent was added to each sample solution. Prior to adsorption, the initial pH value of each test solution was adjusted by adding a small amount of either 0.1 mol/L HNO3 or 0.1 mol/L NaOH, and was monitored by a digital pH meter Metrohm (model 744). The mixture was shaken at 300 rpm (IKA-OS2

Fig. 2. (A) TEM micrograph of the aluminum nano-oxide (equipment: JEOL, Mod. JEM), and (B) XRD spectrum of the aluminum nano-oxide (equipment: Bruker D-8 advance).

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Box–Behnken design as optimization approach A Box–Behnken design (BBD), which is a rotatable three-level design, was used for the exploration of a quadratic response surface and the construction of a second order polynomial model. A number of experimental runs are followed by N = 2k(k  1) + C0, where k is the number of factors and C0 is the number of center points. The Box–Behnken design allows for an estimation and interpretation of the interaction between various factors at a time during an optimization process [27,28,36–38]. Table 1 gives the factors and their levels used in the Box–Behnken design. According to the aforementioned equation, for four factors and three center points, 27 experimental runs have been implemented. All trials of the response (slope) were carried out and duplicated through running the BBD, and the results are illustrated in Table 2. By applying multiple regression analysis to the experimental data, the following second-order polynomial equation was established to explain the adsorption response (removal percent):

y ¼ b0 þ

k k k1 X k X X X bi xi þ bii x2i þ bij xi xj þ e i¼1

i¼1

ð3Þ

i¼1 j¼2

where y = predicted response (removal percent), b0 = offset term, bi is the coefficient of linear effect, bii is the coefficient of squared effect, bij is the coefficient of interaction effect and e is random error. They were estimated through multiple non-linear regression analysis. Surfaces were then built using the quadratic model for the statistically significant variables. The Minitab 16 statistical software was used for regression analysis of the data obtained and to estimate the coefficient of regression equation. Fig. 3. SEM images of the aluminum nano-oxide (A) and SMA (B).

Results and discussion Basic Orbital Shaker, Germany) at a known temperature for 60 min. After adsorption, the sample was centrifuged to separate the adsorbent from the solution. Determination of cationic dyes in binary mixtures

The assay of BG and CV in a single solution

A UV–Vis spectrophotometer (Analytikjena SPECORD250) was used for analysis of the studied dyes. The absorption spectra of the studied dyes were recorded in 1 cm quartz cells at a scan rate of 50 nm/s with a fixed slit width of 1 nm. The following equations have been utilized for determining the removal percentage (R %) and dye capacity uptake per unit of adsorbent (q) from the aqueous solution:

R%¼

q¼

ðC 0  C e Þ  100 C0

ðC 0  C e ÞV m

In the first part of this study, the determination of concentrations of the remaining dyes (BG and CV), both in single and binary mixture solutions, has been accounted for.

ð1Þ

ð2Þ

where C0 is the dye initial concentration (mg/L), Ce the equilibrium dye concentration, V the volume of solution (L) and m the sorbent mass (g). Fourier transforms infrared (FTIR) spectroscopy The Fourier transform infrared (FTIR) spectra of alumina NPs, SMA and dye-loaded SMA in the range of 400–4000 cm1 were taken using a Unicam-Galaxy series FT-IR 5000. These spectra were used to identify adsorption events of either SDS or dyes onto alumina NPs. Therefore, 0.1 g of SMA was loaded along with a 10 mL dye solution in the same concentration of 0.83 mmol/L (400 mg/L BG and 338 mg/L CV) in 250 mL Erlenmeyer flasks and shaken at a rate of 300 rpm for 60 min at room temperature.

The zero order absorption spectra of BG and CV were used for the analysis of dyes in single dye solutions. The single solutions of BG and CV were prepared and the zero order absorption spectra of these solutions recorded were between 450 and 700 nm, as given in Fig. 4A. The calibration curves were prepared at kmax of each dye [3,15,16,28]. The simultaneous determination of BG and CV in binary solutions For the simultaneous analysis of BG and CV dyes in a binary mixture, the binary mixture of these dyes were prepared and zero order absorption spectra were recorded (Fig. 4A). In binary mixtures, the absorption spectra of BG and CV dyes overlapped and showed interference between the zero order spectra of BG and the CV dyes, so their concentrations could not be simultaneously determined by direct absorbance measurements [3,4,7,15,28,29,32] (Fig. 4A). The concentration of BG and CV in binary mixtures can be rightly determined by a first order derivative spectrophotometry (Fig. 4B). The

Table 1 Experimental ranges and levels of the effective variables. Coded values (Xi)

t (min)

m (g)

CCV (mg/L)

CBG (mg/L)

1.00 0 1.00

2 31 60

0.010 0.035 0.060

169 507 845

200 600 1000

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Table 2 Box–Behnken matrix design and obtained response results. Standard order

t

m

CBG

CCV

%RBG

%RCV

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

2 60 2 60 31 31 31 31 2 60 2 60 31 31 31 31 2 60 2 60 31 31 31 31 31 31 31

0.01 0.01 0.06 0.06 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.01 0.06 0.01 0.06 0.035 0.035 0.035 0.035 0.01 0.06 0.01 0.06 0.035 0.035 0.035

600 600 600 600 200 1000 200 1000 600 600 600 600 200 200 1000 1000 200 200 1000 1000 600 600 600 600 600 600 600

507 507 507 507 169 169 845 845 169 169 845 845 507 507 507 507 507 507 507 507 169 169 845 845 507 507 507

19 36 37 79 72 36 43 19 28 68 16 36 47 84 21 50 28 71 13 26 43 77 22 55 56.6 56.6 58.5

30 47 83 98 97 74 63 58 81 90 42 75 44 96 35 85 64 91 56 72 63 95 19 90 87 86 85

first derivative is the rate of change of absorbance against wavelength. It starts and finishes at zero, and passes through zero at the same wavelength as kmax of the absorbance band, with first a positive and then a negative band, and with the maximum and minimum at the same wavelengths as the inflection points in the absorbance band. The derivation of zero order spectra can lead to a separation of overlapped signals, and reduce the effect of spectral background interference caused by the presence of other compounds in a sample [29–35]. Hence, BG and CV dye concentrations in binary mixtures were determined by measuring the absorbance signal at the first order derivative wavelength. According to the zero-crossing derivative method it is necessary that zero-crossing wavelengths do not change with the varied concentrations of related species [29–35]. To evaluate the condition, changes in the pre-mentioned zero-crossing wavelengths for BG and CV were tested in the presence of different concentrations of another species. The calibration graphs for the determination of BG in the presence of CV were constructed by measuring derivative amplitudes at the zero crossing points of CV (588 nm). Similarly, calibration graphs were constructed by the measurement of derivative amplitudes at the zero crossing point of BG (626 nm) (Fig. 4B). In order to test the mutual independence of the analytical signals of BG and CV, calibration graphs were constructed for standard solutions containing various amounts of BG in the presence of 5 mg/L of CV. A similar procedure for standard solutions containing various amounts of CV in the presence of 10 mg/L of BG was performed. The similarity observed between regression equations of pure dye and the mixed solution suggested no interferences in the estimation of one dye in the presence of the other. The regression equations and coefficients of determinations of obtained calibration graphs are given in Table 3. The derivative amplitudes measured at 588 nm were found to be independent of the concentration of CV. Similarly, derivative amplitudes measured at 626 nm were found to be independent of the concentration of BG. The validity of the proposed method was determined in several synthetic binary mixtures containing BG and CV. However, recovery studies confirmed that the simultaneous determination of BG and CV contents in binary solutions could be accurately accomplished by taking the first-order derivatives of the spectrophotometric method [30–32]. The results obtained are given in Table 4. Effect of surfactant coverage on alumina NPs In order to investigate the effect of modification of adsorbent, the removal efficiency of BG and CV by alumina NPs and SMA is shown in Fig. 5 [16]. It is found that the adsorption of BG and CV from an aqueous solution increased when using SMA, due to interaction between dyes and the surfactant. Fig. 5 shows that 0.05 g of alumina can remove 25% of CV (100 mg/L) and 45% of BG (100 mg/ L) from an aqueous solution, while SMA can remove 96% CV and 95% of BG. In a preliminary test, both CTAB and SDS were used to enhance the hydrophobicity of alumina.

Table 3 Determination of BG and CV in standard solutions by first-derivative spectrophotometric method.

Fig. 4. (A) Absorption spectra of: CV (7 mg/L); BG (14 mg/L); and their mixture spectrum and (B) first derivative spectra of: CV, BG and their mixture spectrum.

Sample Composition of solutions (mg/L) BG

Composition of solutions (mg/L) CV

Regression equations (at R2 588 nm for BG; at 626 nm for CV)

LOD (mg/ L)

1 2 3 4

0 5 1–15 1–15

D = 0.001C + 0.0012 D = 0.001C + 0.0010 D = 0.002C + 0.0010 D = 0.002C + 0.0070

0.3 0.3 0.5 0.5

1–20 1–20 0 10

0.997 0.998 0.999 0.999

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J. Zolgharnein et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 137 (2015) 1016–1028 Table 4 Simultaneous determination of BG and CV in synthetic binary mixtures. Taken (mg/L) BG

Taken (mg/L) CV

Found (mg/L) BG

Found (mg/L) CV

Recovery % BG

Recovery % CV

8 4 6 14 12 10 5

10 15 9 5 3 7 17

7.8 4.1 5.9 14.3 11.7 10.2 4.9

10.3 15.4 9.3 5.1 3.1 6.8 17.4

103.0 102.7 103.3 102.0 103.3 97.1 102.3

97.5 102.5 98.3 102.1 97.5 102.0 98.0

99.7

102.0

Mean

120 100

%R

80 60 40 20 0 Alumina-BG

SMA-BG

Alumina-CV

SMA-CV

Fig. 5. Comparison of adsorption efficiency of the CV (100 mg/L) and BG (100 mg/L) by alumina and the SMA using sample volume = 10 mL; sample’s pH = 4.0; m = 0.05 g; shaking time = 60 min.

The removal percent for BG and CV were obtained in the two types of surfactant modified systems. However, the adsorption of BG and CV was low, with different amounts of CTAB. Therefore the CTAB surfactant was not adopted in this study. Effect of pH on the removal of BG and CV The pH of an aqueous medium is an important factor that may have an influence on the uptake of the dyes. The chemical characteristics of both dyes and modified sorbents vary with pH. Studies were carried out to see the effect of pH, in the range of 2–10. In this study, initial CV concentration was fixed at 70 mg/L, initial BG concentration was 50 mg/L and the adsorbent dose was 0.005 g. Fig. 6 shows that the removal of BG and CV increases with an increase in pH up to 8, then gradually decreases. With the increased pH, the free ends of SDS molecules (in the bilayer structure of SDS on alumina) become more negatively charged [17] and react with cationic dyes, causing more removal (Fig. 7), but at a pH greater than 9 SDS molecules are desorbed from the alumina surface and cause less removal of dyes.

100

%R

80 60 R-CV

40

R-BG 20

1

3

5

7

9

11

pH Fig. 6. Effect of pH on CV (70 mg/L) and BG (50 mg/L) removal by SMA.

Fig. 7. Schematic presentation of dye removal mechanism by SMA.

Characterization of SMA FTIR analysis was conducted to determine the functional groups that imply SDS and dyes are adsorbed on the surface of alumina NPs. The FTIR spectra of alumina NPs (a), SMA (b) and SMA after the adsorption process of BG and CV loaded on SMA (c) are presented in Fig. 8. In spectrum (a), absorption bands in the range of 400–1000 cm1 are attributed to the stretching and bending vibrations of AlAO bonds in AlAOAAl [16,39–41]. The peak at 1635 cm1 corresponds to the bending vibrations of the OAH bond of chemisorbed water, and the broad absorption peak appearing around 3447 cm1 is attributed to the stretching mode of the OAH bond of the surface adsorbed water and hydroxyl groups [16,31–41]. The spectrum of the SMA (b) shows new peaks at 2922 cm1, 2852 cm1 and 1244 cm1 that are related to the CAH stretching vibrations of CH, CH2, CH3 and SO3 groups of surfactants. The comparison of FTIR spectra of pure alumina NPs (a) and SMA (b) shows that the alumina NPs surface was well modified by SDS. Also, various absorption peaks appearing in the range of 1000–2000 cm1 (in (c)) are attributed to the adsorbed BG and CV dyes on the SMA (Table 5). The size and morphology of alumina nanoparticles by SEM image is illustrated in Fig. 3A. As shown in Fig. 3A, the naked alumina nanoparticles had a mean diameter of 25 nm. After modification, the nanoparticles prepared were in the range 30–40 nm in diameter. This shows that the alumina nanoparticles had been completely coated by the SDS, and thus that the coating process significantly results in agglomeration and changes in the size of the particles. Although some agglomerations of nanoparticles causing irregularity in the shape and size of the nanoparticles can also be observed in Fig. 3B, it was found that these agglomerations had no obvious effect on adsorbing applications of the nanoparticles. It was clear that modified nanoparticles had a mean diameter of 35 nm since the agglomeration was not serious. The specific surface area was determined using the BET technique. The results showed that the average specific surface area of alumina nanoparticles was 110 m2/g. It can be concluded from these values that this type of alumina is made up of nanoparticles with relatively large specific surface area.

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experimental ranges and their levels for Box–Behnken matrix design are shown in Table 1. The derived matrix design for running BBD is shown in Table 2. By applying multiple regression analysis to the experimental data, the experimental results of the BBD are fitted with a full second-order polynomial equation. The empirical relationship between responses (R) and effective variables obtained as coded values is given thus:

RCV ¼ þ86:265 þ 9:735t þ 25:7190m  6:194C BG  12:722C CV  13:669m2 þ 9:75m  C CV  8:201t 2  7:515C 2BG  5:915C 2CV  2:566t  C BG þ 6t  C CV þ 4:417C BG  C CV

ð4Þ

RBG ¼ þ57:122 þ 14:583t þ 16:167m  15C BG  11:083C CV  14:198t 2  7:573C 2BG  6:948C 2CV þ 6:25t  m  7:5t  C BG  2m  C BG  5t  C CV þ 3C BG  C CV

Fig. 8. FT-IR spectra of alumina (a) SMA (b) and (BG and CV)-lauded SMA (c).

Table 5 Some functional groups and their wavelength ranges. Wavelength range (cm1)

Assignment

3500–3200 3000–2850 3000–2850 1750–1680 1670–1640 1640–1500 1450–1375 1375–1300 1300–1000 1350–1000

Bonded hydroxyl groups (OH) CAH methyl and methylene groups CAH stretching C@O carbonyls Carboxylic groups Carboxylic groups Symmetric bending of CH3 CAO stretching of COOH ASO3 stretching OAH alcohols (primary and secondary) and aliphatic ethers CAO stretching of COOH CAH bending OAAlAO stretching and bending

1300–1000 990–690 400–1000

Response surface as optimization approach Box–Behnken design was applied as an appropriate response surface approach for an optimization approach in this study [26–28,36–38]. Four important factors, such as the initial concentration of dyes (BG and CV), Cd (mg/L), contact time (t) and the sorbent mass (m) are considered as independent variables. Their

ð5Þ

where R represents the predicted dye removal percent. ANOVA test results for the model terms and coefficients of the suggested second-order equation are illustrated in Table 6. Fisher’s F test through the analysis of variance was run, where the larger F-values and the smaller P-values indicate the more significant terms of the model. The values of p < 0.05 also indicate significant regression at the 95% confidence level. The ‘‘Lack of Fit’’ p-values imply that ‘‘Lack of Fit’’ is not significant, relative to pure error for this study. Goodness-of-fit for these models was also evaluated by determination of coefficients (R2) and adjusted R2 (R2adj). A higher value of R2 is desirable, as it is interpreted as the percentage of variability in the response explained by a statistical model. Thus, the present values of R2 and R2adj reported in Table 7 are good. Furthermore, the model adequacy has been investigated by examination of the residuals (e = Rexp  Rpredic). The residual analysis outlines a good concordance between experimental and predicted responses. Further, the normal probability plot shows that the distribution of residuals is normal and the model satisfies the assumptions of the analysis of variance [26–28]. The response surface plots are shown in Figs. 9 and 10 and clearly represent the effects of experimental variables and their mutual interactions on responses. The predicted models are visualized by the equation of responses, as a function of two independent variables, while the third is kept constant (at the middle point). These surfaces also clearly show the interaction between the main factors: m  t, CBG  m, CBG  t, CCV  t, and CBG  CCV (Figs. 9a–e and 10a–d). According to the ANOVA results, it is clear that all of the main effects are significant (P < 0.05); especially as the contact time (t) and sorbent mass (m) have large variances in the removal percent. As the model explains, contact time and sorbent mass have a positive effect, which means that increasing their levels from low to high increases the removal percentage. In addition, increasing initial concentrations of dyes decreases the removal efficiency. Table 7 illustrates the interaction between the main factors and reveals that there are some interactions between some factors. The presence of interactions means that the factors may affect the response interactively and not in an independent way; that is, their combined effect is greater or lesser than that expected for the straight addition of the effects. Also the removal efficiency and the adsorption capacity of the adsorbent for each component in a multi-dye system decreased when another dye concentration was increased, clearly indicating the competition between component dyes for the available adsorbent surface area. Furthermore, the simultaneous optimization of RBG and RCV is more favorable from an experimental perspective. The highest removal efficiency with the least amount of adsorbent usage is more favorable [16,27,42,43]. For this purpose the desirability function, as a well known approach,

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by nonlinear least-square regression analysis, using Microsoft Excel solver version 2007, and are presented in Table 8.

Table 6 Analysis of variance (ANOVA) for the suggested model. Source

DFa

SSb

MSc

RBG Regression Residual error Lack-of-fit Pure error

12 14 12 2

11753.7 25.5 23.1 2.5

Total

26

11779.3

RCV Regression Residual error Lack-of-fit Pure error

12 14 12 2

13209.3 34.6 33.5 1.2

Total

26

13,244

Fd

Pe

979.48 1.82 1.92 1.23

537.24

0.000

1.56

0.456

1100.78 2.47 2.79 0.59

444.76

0.000

4.75

0.187

Single-component adsorption isotherm models

Bolds are insignificant (p < 0.05). a DF = degree of freedom. b SS = sum squares. c MS = mean square (SS/DF). d F = Fishcer ratio. e P = significance level.

was used for multi-response optimization [3,16,27,28,42,43]. Simultaneous optimization of both responses (RBG and RCV) was carried out, and 100% of the goal of the desirability function was achieved. This may be achieved through simultaneous maximization of %RBG and %RCV. It yields RBG% = 84 and RCV% = 98 with 100% desirability (D = 1) in t = 50 min, sorbent dose m = 0.036 g, CBG (Initial BG concentration) = 215 mg/L and CCV (Initial CV concentration) = 170 mg/L. These optimum conditions were found through running Minitab 15 software.

The current research presents a method of direct comparison of the isotherm fit of some models to enable the best-fit and best isotherm parameters to be obtained. Some isotherms such as Langmuir, Freundlich and Sips models were studied in detail [44,45]. The Langmuir model suggests that dye removal from the aqueous phase occurs on homogeneous surfaces by monolayer adsorption without any interactions between sorbate molecules, and the adsorption of each molecule onto the surface has equal adsorption activation energy [42–45]. Conversely the Freundlich isotherm assumes a heterogeneous surface with anon-uniform distribution for heat adsorption over the surface [15,16,44–46]. Sips isotherm is a combined form of Langmuir and Freundlich expressions deduced for predicting the heterogeneous adsorption systems and circumventing the limitation of the rising adsorbate concentration associated with the Freundlich isotherm model. At low adsorbate concentrations, it reduces the Freundlich isotherm; while at high concentrations, it predicts a monolayer adsorption capacity characteristic of the Langmuir isotherm. As a general rule, the equation parameters are governed mainly by the operating conditions, such as the alteration of pH, temperature and concentration [44–46]. The Langmuir, Freundlich and Sips isotherms are represented by the following equations, respectively [44–46] Langmuir:

qe ¼ Equilibrium modeling

qmax bC e 1 þ bC e

ð6Þ

Freundlich:

The equilibrium isotherm plays an important role in designing adsorption systems. The adsorption isotherm deals with the relation between the mass of the dye adsorbed at a particular temperature, the pH, particle size and the liquid phase of the dye concentration [7,8,15,16]. Thus, the adsorption behavior of BG and CV with SMA was characterized by some important isotherms, such as Langmuir, Freundlich and Sips models. Equilibrium adsorption data were fitted to these models using the nonlinear regression method [15,16,42,43]. The simultaneous adsorption data of BG and CV on the SMA has also been fitted to the multi-component isotherm models, viz. Extended-Langmuir and Extended Sips isotherms [15,16, 29–32,44,45]. Therefore, the models’ parameters were obtained

qe ¼ K F C 1=n e

ð7Þ

Sips: n

q¼

qs ðbs C eq Þ s n ½1 þ ðbs C eq Þ s 

ð8Þ

where qmax is the maximum amount of adsorption (mg/g); qe is the adsorption capacity at equilibrium (mg/g); b is the adsorption equilibrium constant (L/mg); Ce is the equilibrium concentration of sorbate in the solution (mg/L); KF is the constant (mg/g), representing the adsorption capacity; ns is the Sips constant; and n is the constant depicting the adsorption intensity.

Table 7 Estimated effects and coefficients for the suggested second-order models of RBG and RCV. RBG

RCV

Predictor

Coef.

SE

CoefT

P

Predictor

Coef.

SE

CoefT

P

Constant t m CBG CCV t2 C2BG C2CV tm t  CBG t  CCV m  CBG CBG  CCV

57.122 14.583 16.167 15.0 11.083 14.198 7.573 6.948 6.25 7.5 5.0 2.0 3.0

0.5811 0.3898 0.3898 0.3898 0.3898 0.5512 0.5512 0.5512 0.6751 0.6751 0.6751 0.6751 0.6751

98.307 37.414 41.476 38.483 28.435 25.757 13.738 12.605 9.258 11.109 7.406 2.962 4.444

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.010 0.001

Constant t m CBG CCV t2 m2 C2BG C2CV t  CBG t  CCV m  CCV CBG  CCV

86.265 9.735 25.719 6.194 12.722 8.201 13.669 7.515 5.915 2.566 6.0 9.75 4.417

0.9083 0.4541 0.4541 0.4541 0.4541 0.6812 0.6812 0.6812 0.6812 0.7866 0.7866 0.7866 0.7866

94.975 21.436 56.632 13.638 28.013 12.038 20.065 11.032 8.682 3.262 7.628 12.395 5.615

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.000 0.000 0.000

R-Sq(BG) = 99.87%, R-Sq (adj)(BG) = 99.60%. R-Sq(CV) = 99.74%, R-Sq (adj)(CV) = 99.51%.

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Fig. 9. Response surface plots showing effective factors and their mutual effects on removal efficiency of BG while the other variables are at center level: (a) the effect of t (time), initial BG concentration, (b) the effects of sorbent amount (m), t (time), (c) the effect of initial BG concentration, initial CV concentration, (d) the effect of sorbent amount (m), initial BG concentration, and (e) the effect of t (time), initial CV concentration.

To quantitatively compare the applicability of each model, an error function is required. As a result, a Chi-square (v2) test, the coefficient of determination (R2) and normalized standard deviation (Dq) were employed as judging criteria to find out the best-fitted isotherm model for the experimental equilibrium data in non-linear regression analysis [15,16,42,43]. These error functions are given as [46–48].

v2 ¼

  X qe;cal  qe;exp 2 qe;exp

ð9Þ

 P e;cal 2 qe;exp  q ffi R2 ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi      e;cal 2 þ qe;exp  qe;cal 2 qe;exp  q sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ½ðqe;exp  qe;cal Þ=qe;exp 2 Dq ¼ n1

ð10Þ

ð11Þ

where n is the number of data points, qe,exp is the equilibrium adsorption capacity from the experiment (mg/g), and qe,cal is the equilibrium capacity, calculated according to the dynamic model

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Fig. 10. Response surface plots showing factors and their mutual effect on removal efficiency of CV while the other variables are at center level: (a) the effects of initial BG concentration, t (time), (b) the effect of initial CV concentration, t (time), (c) the effect of initial BG concentration, initial CV concentration, (d) the effect of initial CV concentration, sorbent amount (m).

Table 8 Isotherm parameters values for the adsorption of BG and CV on SMA in single and binary solutions. Langmuir

qmax (mg/g)

b (L/mg)

R2

Dq

v2

CV (sin) BG (sin) CV (bin) BG (bin)

254.297 168.593 200.437 143.041

0.0468 0.0177 0.0582 0.00713

96.47 94.86 98.01 98.57

0.0128 0.106 0.050 0.195

19.8 7.5 2.9 13.7

Freundlich

kf (mg/g)

n

R2

Dq

v2

CV (sin) BG (sin) CV (bin) BG (bin)

83.209 32.692 65.887 11.859

5.598 4.078 5.817 2.838

81.55 94.61 87.56 96.80

0.216 0.073 0.180 0.145

37.3 4.3 24.2 5.46

Sips

qs (mg/g)

bs (L/mg)

ns

R2

Dq

v2

CV (sin) BG (sin) CV (bin) BG (bin)

263.617 207.048 200.121 168.883

0.0468 0.0101 0.0583 0.0046

0.834 0.619 1.0116 0.753

96.98 97.72 98.01 99.09

0.086 0.0467 0.050 0.106

8.7 1.8 3.0 2.97

Extended-Langmuir

qmax (mg/g)

b1

b2

R2

Dq

v2

CV (bin) BG (bin)

204.873 157.517

0.057 0.0078

0.0014 0.0087

99.82 99.80

0.050 0.160

3.39 10.6

Extended-sips

qmax (mg/g)

b1

b2

n1

n2

R2

Dq

v2

CV (bin) BG (bin)

198.966 173.602

0.223 0.0146

3.111 4.995

1.097 0.754

0.263 0.0819

99.85 99.86

0.048 0.090

3.05 2.7

Conditions: 10 mL solution, pH: 4 and 0.03 g of SMA in sin. and bin. systems.

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(mg/g). A small value of v2 indicates that data obtained from the model is similar to the experimental value. These criteria provide a numerical estimate to measure the goodness of fit for a given mathematical model to the data. The parameters of isotherm models listed in Table 8 are determined by means of a non-linear regression method [46,47]. The comparison of these criteria for studied isotherms leads to a different conclusion. However, judging according to R2, Dq and v2, the order of goodness of fit for considered isotherms are: Sips > Langmuir > Freundlich. The result in Table 8 shows also that the equilibrium pattern fits well with the sips isotherm rather than others isotherms.

Adsorption kinetic study To understand the mechanisms of the adsorption process, a kinetic study is helpful [44–48]. Kinetic studies of sorption for BG and CV on SMA were carried out at the initial concentrations of 400.0 mg/L and 338 mg/L, respectively (in 150 mL flask during shaking at 300 rpm), for contact times in the range of 2–90 min. As depicted in Fig. 11a and b, the adsorption of BG and CV was rapid and an interaction period of 20 min supplied more than 50% adsorption for every dye. For understanding the mechanism of the adsorption process and determining the rate of the dominating step, the adsorption data were analyzed using three simple kinetic models, such as the pseudo-first-order, the pseudosecond-order, and the intra-particle diffusion [44–48].

Multi-component adsorption isotherm models The majority of studies on adsorption of dyes by different kinds of adsorbents have focused on single dye uptake [1–8]. In contrast to this ideal condition, various types of dyes may be in waste water. Another discouraging fact is that the equilibrium modeling of multi-component adsorption, which is essential in the design of treatment systems, has often been neglected. In practice, an examination of the effects of binary dyes in various combinations is deemed to be more representative than single-component studies [15,16,24–27]. One of the major concerns arising from the adsorption of dyes from wastewater is the simultaneous presence of miscellaneous dyes. The interference and competition between different dyes and solvents, as well as dyes and adsorption sites, are significant enough to be taken into account, leading to a more complex mathematical formulation of the equilibrium [44–46]. Given the adsorption of dyes in real systems involving more than one component, adsorption equilibria engaging in competition between molecules of different types is warranted for a better understanding of the system and for design purposes [29,31,32,48]. Consequently, some isotherms were developed to describe equilibrium in such systems [45–48]. Extended-Langmuir:

qe;i ¼

qi bi C i P 1 þ nj¼1 bj C j

Pseudo-first-order model The pseudo-first-order equation is given as [15,16,42–46,49– 52]:

lnðqe  qt Þ ¼ ln qe  K 1 t

ð14Þ

where qe and qt are the dye adsorbed at equilibrium and time t (mg/ g), respectively. K1 is the rate constant for pseudo-first-order adsorption (min1). The values of the adsorption rate constant (K1) for dye adsorption on SMA were determined from the plot of ln(qe  qt) against t (not shown here). The low value of the coefficient of determination shows that adsorption of dyes on SMA does not follow the pseudo-first-order kinetic (Table 9). Thus, the pseudo-second-order model was investigated next.

ð12Þ

Extended-Sips:

h i nsi nsi nsj qi ¼ qs ðbsi C eqi Þ = 1 þ ðbsi C eqi Þ þ ðbsj C eqj Þ

ð13Þ

The parameters of isotherm models listed in Table 8 were determined by means of a non-linear regression method. The high determination coefficients (R2: 96.98–99.86) indicate that both the Extended Langmuir and Extended Sips models were also very suitable for describing the adsorption equilibrium of dyes by the adsorbent in a binary system, as can be seen in Table 8. The applicability of both models implies that monolayer adsorption, as well as heterogeneous surface conditions, may co-exist under the adjusted recent experimental conditions [29,31,32,48]. As indicated in Table 8, the adsorption capacity of adsorbents for each dye in multi-component systems decreased, clearly indicating the competition between component dyes for the available surface area. Table 8 exhibited a higher maximum adsorption capacity for CV than that of BG. However, the addition of competitive dyes decreased the q value of the dye considered [15,16,29– 31]. The presence of 200 mg/L BG reduced the maximum CV uptake capacity of the adsorbent from 254 to 200 mg/g, while the maximum uptake capacity of the adsorbent for BG decreased from 168 to 143 mg/g in the simultaneous presence of 200 mg/L CV, compared to the mono-dye conditions.

Fig. 11. The effect of contact time on dye removal efficiency (m: 0.05 g, pH: 4, CBG: 400 mg/L, CCV: 338 mg/L and sample volume = 10 mL) in single and binary systems, and Pseudo second order adsorption kinetics of dyes onto SMA (a) (1) BG (sin.), (2) BG (bin.) and (b) (1) CV (sin.), (2) CV (bin.).

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Pseudo-second-order model The pseudo-second-order model is represented as [15,16,42– 46,49–52]:

t 1 1 ¼ þ t qt k2 q22 q2

ð15Þ

where q2 is the maximum adsorption capacity (mg/g) for the pseudo-second-order adsorption, qt is the amount of dye adsorbed at time t (mg/g), and k2 is the equilibrium rate constant of pseudo-second-order adsorption (g/mg min). The q2 is obtained from the slope of t/qt versus t (Fig. 11a,b) and k2 is obtained from the intercept. The best-fitted model was chosen according to the linear regression coefficient of the determination of R2 values. The qe,exp and qe,cal values for the pseudo-first-order model and pseudo second-order models are also shown in Table 9. The qe,exp and the qe,cal values from the pseudo-second-order kinetic model are very close to each other, and also the calculated determination coefficients, R2, are also closer to unity for pseudo-second order kinetics than for the pseudo first-order kinetics. Therefore, the adsorption kinetic can be explained more appropriately by the pseudosecond-order kinetic model than the first-order kinetic model for the adsorption of dyes by SMA. Intra-particle diffusion study Besides adsorption at the outer surface of the adsorbents, there is also a possibility of intra-particle diffusion from the outer surface into the pores of the material [15,16,40–46,49–52]. The possibility was explored by plotting the amount of dye adsorbed per gram of adsorbent vs. t1/2. According to Weber and Morris’ equation [52]:

qt ¼ ki t 1=2 þ C

ð16Þ

where qt is the amount of dye adsorbed at time t in mg/g, t is the time of contact (min) and Kp is the intra-particle diffusion constant (mg/g min1/2). The plot of qt against half power of time (t1/2) is a straight line which does not pass through the origin. This shows that intra-particle diffusion is not the only rate-controlling step [15,16,42–46,49–52]. In Table 9 the C values give an idea of the thickness of the boundary layer, i.e., the larger intercepts the greater (C = 46.79, 40.55 in the bin system and 34.55, 35, 89 in the sin system (for both CV and BG respectively) is the boundary layer effect. It can be deduced that there are three steps that control the rate of adsorption but only one is rate-limiting. The slope of the linear portion is an indication of the rate of adsorption. The lower slope corresponds to a slower adsorption step and diffusion in the bulk phase (external diffusion) is the fastest. The second portion of the plot seems to refer to the diffusion into mesopores and the third one by the lower slope is due to adsorption of the adsorbent into micropores. This pattern implies that the intra-particle diffusion of dye into micropores was the rate-limiting step in the adsorption process of SMA. Ultimately, adsorption capacity for CV was higher than for BG in both single and binary solutions, and in binary solutions the presence of alizarin red restricted the adsorption of indigo

carmine. The difference in adsorption capacity, and in synergism and antagonistic, between the two dyes may be mainly attributed to the chemical structure of each dye. So the higher adsorption capacity and faster kinetic achievement on the surface of a sorbent (Fig. 11) may be more related to the smaller molecular size of BG than CV. The sorbate characteristics and adsorbent specifications affect the equilibrium and kinetic behavior of the adsorption process [15,16,29–32]. Thermodynamic studies The effect of temperature on the adsorption of BG and CV by SMA is investigated at four different temperatures (298, 308, 318 and 328 K). The temperature of the adsorption medium could be important for energy-dependent mechanisms in dye adsorption by SMA. For the increase in temperature from 298 to 328 K, the removal percent of BG and CV showed an increase from 88 to 95.8 and 75.5 to 80.6, respectively, thus indicating the endothermic nature of these adsorption processes. Furthermore in environmental engineering practice, both energy and entropy factors must be considered in order to determine what processes will occur spontaneously. The Gibbs free energy change, DG°, is the fundamental criterion of spontaneity. Reactions occur spontaneously at a given temperature if DG° is a negative quantity. The adsorption process of dyes can be summarized by the following reversible process, which represents a heterogeneous equilibrium. The apparent equilibrium constant (Kc) of the adsorption is defined as [53–55]:

K c ¼ C Be =C Ae

ð17Þ

where Kc is the concentration of dye on the adsorbent at equilibrium. The Kc value is used in the following equation to determine the Gibbs free energy of adsorption (DG°) [53–55].

DG0 ¼ RT ln K c

ð18Þ

The enthalpy (DH°) and entropy (DS°) can be obtained from the slope and intercept of a van’t Hoff equation of DG° versus T.

ln K c ¼

DS0  DH0 =RT R

ð19Þ

where DG° is a standard free energy change (J), R the universal gas constant (8.314 J/mol K) and T is the absolute temperature (K). The values of the standard Gibbs free energy for the adsorption process obtained from Eq. (18) are listed in Table 10. The negative values of DG° confirm the feasibility of the process and the spontaneous nature of adsorption with a high preference of dyes on adsorbents. The standard enthalpy changes of adsorption determined from Eq. (19) were 8.1 kJ/mol and 29.9 kJ/mol, and standard entropy changes were 36.5 and 117.0 J/(mol K) for BG and CV, respectively. The positive value of DH° suggests the endothermic nature of adsorption. More endothermic behavior of CV than BC may be attributed to the presence of ionic function groups, such as Cl, which, by raising the temperature of CV, has been more solvated and so may facilitate better adsorption onto the modified adsorbent [53]. The positive value of DS° confirms the increased randomness at the

Table 9 Kinetics constants for dye adsorption onto SMA in single and binary systems of dyes. System

BG (sin.) CV (sin.) BG (bin.) CV (bin.)

Pseudo-first order

Pseudo-second order

Intraparticle diffusion

q(Exp) (mg/g)

q1(cal) (mg/g)

k1 (min1)

R2

q2(cal) (mg/g)

k2 (g/mg min)

R2

kp (mg/g min1/2)

C

R2

86.33 96.93 76.92 87.7

– 24.95 34.50 23.29

– 0.035 0.02 0.023

0.57 0.851 0.963 0.925

91 100 83.33 90.90

0.00417 0.00526 0.00244 0.0038

0.999 0.999 0.997 0.999

9.18 11.12 4.13 7.14

35.89 43.55 40.55 46.79

0.926 0.934 0.925 0.923

Conditions: 10 mL solution, pH: 4, T: 25 °C and 0.05 g of SMA for BG (400 mg/L) and CV (338 mg/L) in sin. and bin. systems.

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Table 10 Values of thermodynamic parameters for dye adsorption onto SMA in binary system of dyes. Temp. (K)

DG° (kJ/mol)

BG

298 308 318 328

2.85 3.09 3.45 3.90

DH° (kJ/mol) 8.1

DS° (J/mol K) 36.46

CV

298 308 318 328

4.93 6.22 7.14 8.53

29.9

116.98

Conditions: 10 mL solution, pH: 4, 0.05 g of SMA for BG (400 mg/L) and CV (338 mg/L).

Table 11 Comparisons of capacity uptake of BG and CV with different adsorbents. Adsorbent

Dye

Qmax (mg/g)

Reference

Bottom ash De-oiled soya Rice husk ash Kaolin Semi-IPN hydrogels Red clay Soil-silver nanocomposite Magnetic nanocomposite Kaolin Phosphoric acid activated carbon (PAAC) Sulfuric acid activated carbon (SAAC) NaOH treated saw dust Saccharomyces cerevisiae Surfactant modified alumina Surfactant modified alumina

C CV BG BG CV BG CV CV CV CV CV BG BG BG CV

4 5 26 65 35 125 1.9 81 47 60 85 58

[7] [7] [56] [57] [58] [59] [60] [61] [62] [63] [63] [64] [65] This study This study

168 254

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