Many dyes have hazardous, toxic and carcinogenic impacts on human and animals. They cause injury to the eyes and skin, cerebral confusion and respiration problems (Yagub et al., Reference Yagub, Sen, Afroze and Ang2014). Among the various types of dyes, cationic dyes are very noxious. Crystal violet (CV) dye is a hydrophilic dye suitable for dying wood, cotton, fibre and some polyesters (Li, Reference Li2010). Crystal violet is toxic and very hazardous to the environment, so removal of this dye from wastewater is essential for human health. Several methods have been investigated for the uptake of dyes from wastewater (García-Montaño et al., Reference García-Montaño, Pérez-Estrada, Oller, Maldonado, Torrades and Peral2008). Among these various methods, the adsorption process is an excellent procedure because it is fast, simple and inexpensive (Azizian et al., Reference Azizian, Haerifar and Bashiri2009; Eris & Bashiri, Reference Eris and Bashiri2016). A suitable adsorbent should be low-cost, non-toxic, easy to use and have a large adsorption capacity (Zhu et al., Reference Zhu, Chen, Liu, Ge, Zhu, Zhu and He2014; Gamoudi & Srasra, Reference Gamoudi and Srasra2018).
Clay minerals are used widely as adsorbents in environmental systems and among them montmorillonite (Mt) is the most widely used clay mineral. Montmorillonite is inexpensive, abundant in nature, non-toxic and has a large specific surface area. The Mt structure contains an octahedral sheet between two tetrahedral sheets and interlayer cations such as Ca2+ and Na+, rendering the Mt layers hydrophilic (Cohen et al., Reference Cohen, Joseph, Lapides and Yariv2005). Montmorillonite has been used for the removal of various dyes, especially cationic dyes, and its adsorption mechanism is an irreversible cation exchange (Bujdák et al., Reference Bujdák, Czímerová and Iyi2008; Czímerová & Ceklovský, Reference Czímerová and Ceklovský2017; Fahn & Fenderl, Reference Fahn and Fenderl2018). Montmorillonite is an effective adsorbent for the removal of various types of pollutants from wastewater (Atta et al., Reference Atta, Al-Lohedan, Alothman, Abdel-Khalek and Tawfeek2015; Chen et al., Reference Chen, Zhou, Fiore, Tong, Zhang, Li, Ji and Yu2016). Atta et al. (Reference Atta, Al-Lohedan, Alothman, Abdel-Khalek and Tawfeek2015) investigated the application of Mt nanogels for the removal of toxic cationic dyes and heavy metals. Acid-treated K10 Mt may be used successfully as an effective adsorbent for the removal of CV from aqueous solution (Sarma et al., Reference Sarma, Sen Gupta and Bhattacharyya2016). Acid treatment increased its surface acidity, cation-exchange capacity, specific surface area and pore volume. In addition, Mt and kaolinite have sufficiently strong affinity towards the cationic dye rhodamine B in aqueous solutions (Bhattacharyya et al., Reference Bhattacharyya, SenGupta and Sarma2014).
Zeolites have been used extensively as adsorbents in environmental systems because of their porous structure, high adsorption capacity, low cost, non-toxic nature and abundance. They have various structures consisting of a three-dimensional network with a negative charge that it is balanced by extra-framework cations such as Na+, K+, Mg2+ and Ca2+ (Sismanoglu et al., Reference Sismanoglu, Kismir and Karakus2010). These cations can be exchanged with cationic dyes in water so that the adsorption of dyes in zeolites might occur via an ion-exchange mechanism (Townsend, Reference Townsend, van Bekkum, Flanigen and Jansen1991; Qiu et al., Reference Qiu, Qian, Xu, Wu and Wang2009; Humelnicu et al., Reference Humelnicu, Băiceanu, Ignat and Dulman2017). Hence, kaolin, bentonite and zeolite are suitable adsorbents for the removal of methylene blue and Congo red from aqueous solutions (Vimonses et al., Reference Vimonses, Lei, Jin, Chow and Saint2009; Rida et al., Reference Rida, Bouraoui and Hadnine2013). Sodium bentonite displayed the best adsorption capacity (19.9 mg g−1) for Congo red removal, followed by kaolin (5.6 mg g−1) and zeolite (4.3 mg g−1) (Vimonses et al., Reference Vimonses, Lei, Jin, Chow and Saint2009).
Another adsorbent used for dye removal is activated carbon because it has a high adsorption capacity and it can adsorb both cationic and anionic dyes (Başar, Reference Başar2006; Peláez-Cid et al., Reference Peláez-Cid, Herrera-González, Salazar-Villanueva and Bautista-Hernández2016). Merck activated carbon is a very effective adsorbent for dyes due to its high adsorption capacity, but it is expensive (Azizian et al., Reference Azizian, Haerifar and Bashiri2009; Eris & Bashiri, Reference Eris and Bashiri2016). Commercial activated carbon has greater adsorption capacities for direct yellow, direct red and direct blue dyes than raw kaolinite and Mt (Yavuz & Aydin, Reference Yavuz and Aydin2006).
In this study, the adsorption performance of zeolite-Mt and Merck activated carbon is compared. It will be shown that a low-cost adsorbent such as zeolite-Mt has a high adsorption capacity compared to commercial and expensive adsorbents such as Merck activated carbon. Initially, Mt nanoparticles were used for dye removal from aqueous solution. However, the particle size of nano-Mt was very small and separation from solution was difficult. To solve this problem, natural zeolite was used as an inexpensive support for Mt nanoparticles. This facilitated separation of the adsorbent from solution. The adsorbent's dye removal ability will be investigated in this work.
Response surface methodology (RSM) may be used when dye removal is effected by various parameters, and RSM has been applied in the removal of Coomassie blue dye (de Sales et al., Reference de Sales, Magriotis, Rossi, Resende and Nunes2013). In addition, central composite design (CCD) might be applied to consider the interactions of variables in minimal time (Mousavi et al., Reference Mousavi, Salari, Niaei, Panahi and Shafiei2014; Mousavi & Nakhostin Panahi, Reference Mousavi and Nakhostin Panahi2016). Optimization of the adsorption of azo dyes and heavy metal ions was performed by Alipanahpour Dil et al. (Reference Alipanahpour Dil, Ghaedi and Asfaram2017). Four variables (temperature, pH, adsorbent dosage and initial dye concentration) were studied and modelled in this study. Subsequently, the kinetics, equilibria and thermodynamics of CV adsorption on the zeolite-Mt composite were investigated.
Experimental
Materials and methods
The Mt was obtained from Southern Clay Products, Inc. It had a specific gravity of 0.7 g cm−3, a particle size of 1 nm–2 µm, a cation-exchange capacity of 48 meq 100 g−1 and a specific surface area of ~750 m2 g−1. The Mt sample had the following chemical composition: SiO2 (50.95 wt.%), Al2O3 (19.60), Fe2O3 (5.62), MgO (3.29), CaO (1.97), Na2O (0.98), K2O (0.86), TiO2 (0.62) and loss on ignition (15.45). Clinoptilolite was the main component of the natural zeolite. The specific gravity of zeolite was 0.5–1.1 g cm−3 and its cation-exchange capacity was 2.6 meq g−1. The zeolite sample had the following chemical composition: SiO2 (68.5 wt.%), Al2O3 (11.0), K2O (4.4), Na2O (3.8), Fe2O3 (0.9), CaO (0.6) and loss on ignition (12.0). The CV dye (C25H30N3Cl) and activated carbon were procured from Merck and were used without purification.
Instrumentation
Chemical functional groups were identified by Fourier-transform infrared (FTIR) absorption spectroscopy with an infrared spectrophotometer (FTIR-Magna 500, Nicolet), using the KBr pellet technique. The structure was investigated by powder X-ray diffraction (XRD) with a Philips X'Pert Pro MPD diffractometer using Cu-Kα radiation. The specific surface area was determined with a BEL Sorb Gas Adsorption Analyzer. The morphology was determined by field emission scanning electron microscopy (FE-SEM) (MIRA3, TESCAN). The absorbance spectra were obtained with a T-80 ultraviolet–visible (UV-Vis) spectrophotometer (Shimadzu).
Preparation of zeolite-Mt nano-adsorbent
Natural zeolite was collected from Iran (Semnan). The sizes of the sample particles ranged from 1 nm to 2 µm. For purification of the collected samples, 10 g of zeolite was initially dispersed in 1 L of deionized water, stirred in a shaker at 130 rpm for 24 h, filtered and then washed with deionized water. It was then dried in an oven at 100°C for 12 h and stored. To synthesize the zeolite-Mt composite, 0.5 g of Mt were dispersed in 50 mL of deionized water and stirred for 1 h. The solution was then fixed in an ultrasonic bath for 35 min. Subsequently, zeolite (9.5 g) was added and the suspension was stirred in a shaker at 130 rpm at 35°C for 18 h. Then, the final solution was placed in a furnace at 600°C for 3 h.
Adsorption experiments
To perform the equilibrium studies for CV adsorption on the zeolite-Mt composite, 50 mL of the CV solutions at various concentrations (20–150 mg L−1) was mixed with 0.1 g of zeolite-Mt and then stirred at 130 rpm for 24 h. The equilibrium experiment for CV adsorption on activated carbon was carried out by mixing 0.05 g of adsorbent with 50 mL of the CV solutions at concentrations of 30, 40, 50, 60, 70 and 80 mg L−1 and then stirring at 130 rpm for 24 h. After equilibrium had been achieved, the CV concentration in solution was determined using the UV-Vis spectrophotometer at λmax = 590 nm. The removal percentage of CV was obtained from equation 1:
$$R\% = \displaystyle{{C_0 - C}_{\rm e} \over {C_0}} \times 100\; $$where C 0 and C e are the CV concentrations initially and at equilibrium (mg L−1), respectively. The amount of adsorbed CV on the adsorbent was obtained using equation 2:
$$q_{\rm e} = \displaystyle{{\lpar {C_0 - C}_{\rm e} \rpar V} \over W}$$where V (L) is the volume of the CV solution and W (g) is the mass of the adsorbent used.
The adsorption kinetics of CV adsorption on the zeolite-Mt composite were studied by adding 0.1 g of zeolite-Mt to 50 mL of CV solutions at concentrations of 20, 40, 60 and 80 mg L−1 and then stirring at 130 rpm. The CV concentrations in the solutions were determined at various adsorption times (2–300 min). The amount of CV adsorbed is expressed by equation 3:
$$q = \displaystyle{{\lpar {C_0 - C}_{\rm t} \rpar V} \over W}$$where C t (mg L−1) is the concentration of CV after the corresponding adsorption time.
Experimental design
Experimental design helps to obtain useful information regarding the effects of variables on response with the minimum number of experiments (Alipanahpour Dil et al., Reference Alipanahpour Dil, Ghaedi, Ghaedi, Asfaram, Jamshidi and Purkait2016; Jamshidi et al., Reference Jamshidi, Ghaedi, Dashtian, Ghaedi, Hajati, Goudarzi and Alipanahpour2016). The independent variables were pH (3–11), temperature (25–55°C), adsorbent dosage (0.5–5.0 g L−1) and initial dye concentration (10–100 mg L−1) (Table 1). Overall, 30 experiments were performed, and the matrix of experimental design is presented in Table 2. The correlation of the response and four variables is expressed by equation 4:
$$y = {\rm \beta} _0 + \mathop \sum \limits_{i = 1}^4 {\rm \beta} _ix_i + \mathop \sum \limits_{i = 1}^4 {\rm \beta} _{ii}x_i^2 + \mathop \sum \limits_{i = 1}^4 \mathop \sum \limits_{\,j = 1}^4 {\rm \beta} _{ij}x_ix_j + {\rm \varepsilon} $$where β0 is the constant term, βi is the linear effect term, βii is the quadratic effect term, βij is the interaction effect term, x i and x j are variables, ɛ is the residue of the model and y is the response.
Table 1. Experimental ranges of the independent variables.
Table 2. Independent variables of the CCD matrix and the response values for CV removal.
Results and discussion
Characterization of the adsorbent
The mineralogical and chemical composition and surface morphology of zeolite-Mt were characterized using FTIR, XRD, Brunauer–Emmett–Teller (BET) and FE-SEM analysis.
Fourier-transform infrared analysis
The FTIR spectra of zeolite, Mt and zeolite-Mt before and after CV adsorption are shown in Fig. 1. In the FTIR spectrum of natural zeolite, the bands at 400–1200 cm−1 are attributed to Si–O–Si and Si–O–Al vibrations. The most intense bands at 1063, 1629 and 3426 cm−1 are assigned to asymmetric stretching of external tetrahedral linkages, the stretching vibration mode of adsorbed water in natural zeolite and intermolecular hydrogen bonding, respectively. In the FTIR spectrum of Mt, the bands at ~3431 cm−1 and at 1635 cm−1 are attributed to the –OH stretching and bending vibrations, respectively, of adsorbed water.
Fig. 1. FTIR spectra for (a) zeolite, (b) Mt, (c) zeolite-Mt composite and (d) zeolite-Mt after CV adsorption.
In the zeolite-Mt spectrum, the bands at 3441 and 1642 cm−1 are ascribed to the O–H stretching vibration and H–O–H bending vibrations of water molecules, respectively. The band at ~1069 cm−1 is attributed to Si–O–Si and Si–O–Al asymmetric stretching (Bhattacharyya et al., Reference Bhattacharyya, SenGupta and Sarma2014). The bands at 400–1200 cm−1 are attributed to the vibrations of the Si–O and Al–O bridges. In the spectrum of zeolite-Mt after CV adsorption, the bands at 2921 and 2852 cm−1 are ascribed to C–H stretching and the bands at 1388 and 1461 cm−1 are ascribed to CH3 bending in the CV dye. The presence of these bands confirms dye adsorption on the surface of the adsorbent.
X-ray diffraction analysis
The XRD traces of natural zeolite, Mt nanoparticles and the zeolite-Mt composite are shown in Fig. 2. Natural zeolite consists mainly of clinoptilolite with minor amounts of gypsum and quartz. In the XRD trace of Mt nanoparticle, the reflection at 7°2θ indicates the presence of Na+ in the interlayer. The reflection at 22.5°2θ is attributed to gypsum and the reflection at 27.5°2θ is attributed to quartz. In the XRD trace of the zeolite-Mt composite, the reflection at 17.5°2θ suggests the presence of the mica as an impurity in addition to the minerals determined in the individual zeolite and Mt components (Gautam et al., Reference Gautam, Mudhoo and Chattopadhyaya2013). The intensity of the main reflections in Mt and zeolite decrease in the zeolite-Mt composite after calcination at 600°C.
Fig. 2. XRD traces of (a) zeolite, (b) Mt nanoparticles and (c) zeolite-Mt composite.
Brunauer–Emmett–Teller analysis
The specific surface area, total pore volume and mean pore diameter of natural zeolite, Mt and zeolite-Mt obtained by N2 adsorption–desorption isotherms are listed in Table 3. The Barret-Joyner-Halenda (BJH) surface area of zeolite-Mt was measured at 21.8 m2 g−1. Zeolite has a BET-specific surface area of 24.1 m2 g−1, a pore volume of 0.113 cm3 g−1 and a mean pore diameter of 18.8 nm. The Mt has a BET-specific surface area of 45.8 m2 g−1, a total pore volume of 0.099 cm3 g−1 and a mean pore diameter of 8.7 nm. The N2 adsorption–desorption isotherms of both zeolite and Mt are typical of mesoporous materials. The zeolite-Mt composite has a BET-specific surface area of 19.3 m2 g−1, a total pore volume of 0.12 cm3 g−1 and a mean pore diameter of 24.9 nm. The N2 adsorption–desorption isotherm of zeolite-Mt is indicative of a microporous material (Fig. 3). By comparing the BET-specific surface areas of Mt and zeolite-Mt, it can be concluded that the surface area of Mt decreases after calcination at 600°C.
Fig. 3. N2 adsorption–desorption isotherm of zeolite-Mt.
Table 3. The parameters of zeolite, Mt and zeolite-Mt.
Field emission scanning electron microscopy analysis
The FE-SEM images show the particle size and surface morphology of zeolite, Mt and zeolite-Mt (Fig. 4). The zeolite sample has a porous texture and variable particle size in the micrometre to nanometre range and the Mt crystals also have variable particle sizes. The FE-SEM image of zeolite-Mt shows the presence of zeolite crystals between the Mt layers. The particle size of the zeolite-Mt nano-adsorbent is in the nanometre range.
Fig. 4. FE-SEM images of (a) zeolite, (b) Mt and (c) zeolite-Mt.
Response surface modelling
Regression equation
The analysis of variance (ANOVA) of the R2 values and the model's fit indicate the adequacy of the selected model (Asfaram et al., Reference Asfaram, Ghaedi, Goudarzi and Rajabim2015). Therefore, the ANOVA results show the correlation between the response and the variables to be as follows:
$$\eqalign{ {\rm R} &= 97.92 + 5.26 \times {\rm A} + 0.59 \times {\rm B} + 1.06 \times {\rm C} - 0.27 \times {\rm D} \cr & + 0.77 \times {\rm A} \times {\rm B} + 0.73 \times {\rm A} \times {\rm C} - 0.95 \times {\rm A} \times {\rm D} \cr & - 0.53 \times {\rm B} \times {\rm C} + 0.90 \times {\rm B} \times {\rm D} + 0.85 \times {\rm C} \times {\rm D} - 2.61 \cr & \times {\rm A}^2 - 0.26 \times {\rm B}^2 - 0.52 \times {\rm C}^2 - 0.83 \times {\rm D}^2} $$Therefore, between the variables of pH (A), temperature (B), adsorbent dosage (C) and initial dye concentration (D), pH is the most effective variable in terms of dye removal.
Analysis of variance results
The importance of the various variables was determined by the values of F and p (Table 4). The large F-value (17.60) and the very small p-value (<0.0001) indicate that the model is significant (Iqbal et al., Reference Iqbal, Iqbal, Bhatti, Ahmad and Zahid2016). The lack of fit is 0.1181, and this is not significant relative to the pure error. The ANOVA analysis suggests that pH (p < 0.0001, F = 170.95) and adsorbent dosage (p = 0.0182, F = 7.01) are significant for dye removal, whereas temperature (p = 0.1635) and initial dye concentration (p = 0.5057) are less significant for dye removal. The R2 value (0.9426) shows good agreement between the actual and predicted values. The coefficient of variation (2.08%) indicates that experiments were accurate, while the predicted R2 value of 0.7048 and adjusted R2 value of 0.8891 are in logical agreement. The signal-to-noise ratio is acceptable at 18.045. In addition, the observed and predicted values are in agreement because they are well distributed around the straight line in Fig. 5.
Fig. 5. Experimental data vs the predicted values for dye removal.
Table 4. Analysis of variance of the fitting of the experimental data with the response surface model.
‘Cor total' is used to present the totals of all information corrected for the mean. PRESS (predicted residual error sum of squares) presents a measure of how the model fits each point in the design. ‘Adequate precision' is a signal-to-noise ratio and it compares the range of the predicted values at the design points to the average prediction error.
Central composite design plots
The perturbation plot reveals that, in this model, pH is the most effective variable for removal of CV (Fig. 6). The CCD plots help with investigating the effects of the variable interactions on the response (Satapathy & Das, Reference Satapathy and Das2014). In Fig. 7, pH is denoted as A, temperature as B, adsorbent dosage as C and initial dye concentration as D. The combined effect of pH and temperature on the removal of CV indicates that dye removal increases with both the pH and temperature (Fig. 7a). At higher pH, there is an excess of negative sites on the zeolite-Mt surface for adsorption of cationic dye, and with increasing in temperature the number of active sites on the zeolite-Mt surface increases, thereby increasing the adsorption of CV. The interaction effect of the pH and adsorbent dosage on dye removal shows that dye removal increases with increasing amounts of zeolite-Mt due to abundant active sites (Fig. 7b). The interactive influence of pH and initial dye concentration on dye removal reveals that the removal of dye increases with decreasing initial dye concentration (Fig. 7c). The interaction effect of temperature and adsorbent dosage on dye removal indicates that dye removal increases with increasing temperature and amounts of zeolite-Mt (Fig. 7d). The combined effect of temperature and initial dye concentration on dye removal reveals that removal of CV was increased at low dye concentration (Fig. 7e,f). This is attributed to the fact that with increasing dye concentration at constant adsorbent dosage the number of adsorbing sites decreases gradually, thereby decreasing dye removal.
Fig. 6. The effect of pH (A), temperature (B), adsorbent dosage (C) and initial dye concentration (D) on dye removal efficiency (R%).
Fig. 7. Response surface and counter-plots of (a) pH (A), temperature (B) and dye removal efficiency (R%), (b) adsorbent dosage (C), pH and dye removal efficiency, (c) initial dye concentration (D), pH and dye removal efficiency, (d) adsorbent dosage, temperature and dye removal efficiency, (e) initial dye concentration, temperature and dye removal efficiency and (f) adsorbent dosage, initial dye concentration and dye removal efficiency.
Optimization adsorption conditions
Design Expert (v. 8.0.7.1) software was used to determine the optimum values of the four variables. In this study, maximum removal of CV dye (99.9%) was observed at pH 9, temperature 25°C, adsorbent dosage 2 g L−1 and initial dye concentration of 40 mg L−1. The desirability function assigns numbers between 0 and 1. The desirability function value for zeolite-Mt nano-adsorbent at optimum conditions was found to be 1, and this represents an ideal response value.
Adsorption studies
Adsorption isotherms
The equilibrium studies of CV adsorption on the zeolite-Mt composite were performed by mixing the adsorbent with various dye concentrations (20–150 mg L−1) in conical flasks at optimum pH, temperature and adsorbent dosage values. The flasks were placed in a shaker (130 rpm) for 24 h. The equilibrium studies of CV adsorption on Merck activated carbon were performed by mixing 0.05 g of adsorbent with 50 mL of the CV solution at dye concentrations of 30, 40, 50, 60, 70 and 80 mg L−1. The solutions were placed in a shaker (130 rpm) for 24 h. After equilibrium, the CV concentrations in solutions were determined. Figure 8 shows the adsorption isotherms of CV on the zeolite-Mt composite and the Merck activated carbon.
Fig. 8. Adsorption isotherm of CV dye on zeolite-Mt (at an initial dye concentration of 20–150 mg L−1 and optimum values of pH, temperature and zeolite-Mt dosage) and (b) activated carbon from Merck company (pH 7, temperature 25°C, adsorbent dose 1 g L−1 and initial dye concentrations of 30–80 mg L−1).
Adsorption isotherms may be used to evaluate the maximum adsorption capacity. The non-linear fitting of isotherms was performed and Langmuir (Langmuir, Reference Langmuir1916), Freundlich (Freundlich, Reference Freundlich1906), Temkin (Temkin & Pyzhev, Reference Temkin and Pyzhev1940), Redlich–Peterson (Febrianto et al., Reference Febrianto, Kosasih, Sunarso, Ju, Indraswati and Ismadji2009), Sips (Sips, Reference Sips1948) and Flory–Huggins (Bashiri & Eris, Reference Bashiri and Eris2016) isotherms were investigated. The equations are as follows:
$${\rm Langmuir\ isotherm} \quad q_{\rm e} = \displaystyle{{{\rm K}_{\rm L}q_{\rm max}C_{\rm e}} \over {1 + {\rm K}_{\rm L}C_{\rm e}}}$$
$${\rm Freundlich\ isotherm} \quad q_{\rm e} = {\rm K}_{\rm F}C_{\rm e}^{{1 \over {\rm n}}} $$
$${\rm Temkin\ isotherm} \quad q_{\rm e} = {\rm B}_1{\rm ln}{\rm A}_1 + {\rm B}_1{\rm ln}C_{\rm e}$$
$${\rm Redlich} - {\rm Peterson\ isotherm} \quad q_{\rm e} = \displaystyle{{{{\rm A}C}_{\rm e}} \over {1 + {{\rm B}C}_{\rm e}^{\rm g}}} $$
$${\rm Sips\ isotherm} \quad q_{\rm e} = q_{\rm m}\displaystyle{{{\lpar {{\rm K}_{\rm LF}C_{\rm e}} \rpar }^{{1 \over {\rm n}}}} \over {1 + {\lpar {{\rm K}_{\rm LF}C_{\rm e}} \rpar }^{{1 \over {\rm n}}}}}$$
$${\rm Flory} - {\rm Huggins\ isotherm} \quad \displaystyle{{q_{\rm e}} \over {{\lpar {q_{\rm m} - q_{\rm e}} \rpar }^{\rm n}}} = \displaystyle{{C_{\rm e}} \over {\rm K_{\rm FH}}}$$The Langmuir isotherm model assumes that the adsorbent surface is homogeneous and the Freundlich isotherm is used for heterogeneous surfaces. In these isotherms, q max (mg g−1) is the maximum adsorption capacity, n is the Freundlich constant related to surface heterogeneity and KL and KF are the Langmuir and Freundlich constants, respectively. The Temkin isotherm model assumes that heat of adsorption would decrease linearly rather than logarithmically with coverage. B1 is the constant related to heat of adsorption and A1 (L mg−1) is the Temkin isotherm constant. The Redlich–Peterson model assumes both monolayer and multilayer adsorption. A, B and g are the Redlich–Peterson constants. The Sips isotherm assumes that adsorbent surface is heterogeneous. KLF (L mg−1) is the Sips constant and n is a parameter for surface heterogeneity. The Flory–Huggins isotherm is chosen for the adsorbate with different geometries on the surface and KFH is the Flory–Huggins constant.
All isotherm equations were fitted with experimental data and the results of fittings, and the values of their corresponding coefficients of determinations (R2) are listed in Table 5. Based on the R2 values obtained, the Freundlich isotherm was selected as the most suitable isotherm to describe the CV adsorption. Therefore, the adsorption process is non-ideal, reversible and multilayered, with non-uniform distributions of adsorption heat and affinities over the heterogeneous surface. A favourable adsorption tends to have a Freundlich constant n of between 1 and 10.
Table 5. Parameters for adsorption isotherms on zeolite-Mt and Merck activated carbon.
For CV adsorption on Merck activated carbon, the Sips isotherm fitted the adsorption data better. It assumes that the adsorbent surface is heterogeneous and that the adsorbate forms a monolayer. The Sips isotherm circumvents the limitation of the rising adsorbate concentration when applying the Freundlich isotherm. At low adsorbate concentrations it reduces to the Freundlich isotherm, while at high concentrations it predicts a monolayer adsorption capacity characteristic of the Langmuir isotherm. In a similar study, Sarma et al. (Reference Sarma, Sen Gupta and Bhattacharyya2016) observed that adsorption of CV on raw and Mt treated with 0.25 and 0.50 M sulfuric acid is described better by the Temkin isotherm. In addition, Amodu et al. (Reference Amodu, Ojumu, Ntwampe and Ayanda2015) reported that the adsorption of CV onto magnetic zeolite nanoparticles is best described by the Langmuir adsorption isotherm.
The maximum adsorption capacities of zeolite-Mt under optimum conditions and of activated carbon for CV were 150.52 and 84.11 mg g−1, respectively, using the Sips isotherm at 25°C. Table 6 lists the maximum adsorption capacities of zeolite-Mt with Merck activated carbon and other adsorbents for removal of CV (Senthilkumaar et al., Reference Senthilkumaar, Kalaamani and Subburaam2006; Anirudhan et al., Reference Anirudhan, Suchithra and Radhakrishnan2009; Bertolini et al., Reference Bertolini, Izidoro, Magdalena and Fungaro2013; Chen et al., Reference Chen, Wang, Jin, Chen, Megharaj and Naidu2013; Satapathy & Das, Reference Satapathy and Das2014; Yang et al., Reference Yang, Zhou, Chang and Zhang2014; Amodu et al., Reference Amodu, Ojumu, Ntwampe and Ayanda2015; Hamidzadeh et al., Reference Hamidzadeh, Torabbeigi and Shahtaheri2015; Alipanahpour Dil et al., Reference Alipanahpour Dil, Ghaedi, Ghaedi, Asfaram, Jamshidi and Purkait2016; Ishaq et al., Reference Ishaq, Javed, Amad and Ullah2016). The maximum adsorption capacity of zeolite-Mt (150.52 mg g−1) is greater than for Merck activated carbon and the remaining adsorbents, some of which were difficult to prepare and most of which are more expensive than the zeolite-Mt composite. Therefore, the zeolite-Mt composite is an effective and affordable adsorbent for the removal of CV from water.
Table 6. Comparison of various adsorbents for the removal of CV.
Kinetic models
Kinetic studies of CV adsorption onto the zeolite-Mt composite were carried out at initial concentrations of 20, 40, 60 and 80 mg L−1 of CV with adsorption times up to 300 min at the optimum pH, adsorbent dosage and temperature values (Fig. 9). Pseudo-first-order (Lagergren, Reference Lagergren1898), pseudo-second-order (Ho & McKay, Reference Ho and McKay1999), Elovich (Çoruh et al., Reference Çoruh, Geyikçi and Nuri Ergun2011), modified pseudo-first-order (MPFO; Azizian & Bashiri, Reference Azizian and Bashiri2008), intra-particle diffusion (Wu et al., Reference Wu, Tseng and Juang2009), integrated kinetic Langmuir (IKL) (Marczewski, Reference Marczewski2010) and fractal-like IKL (FLIKL) (Haerifar & Azizian, Reference Haerifar and Azizian2012) kinetic models were used to study the adsorption kinetics. Recently, errors were reported when using the linear fit for a non-linear model as the plot of t/q tvs time always leads to a perfect linear fit because of the linear increase of time in the t/q t variable over time and not because of the properties of the measured dependent variable q (Simonin, Reference Simonin2016). Hence, the kinetic data were fitted by the non-linear regressions:
$${{\rm Pseudo}-{\rm first}-{\rm order\ model}} \quad \displaystyle{q \over {q_{\rm e}}} = 1-{\rm exp}\lpar {-k_1t} \rpar \; $$
$${{\rm Pseudo}-{\rm second}-{\rm order\ model}} \quad \displaystyle{q \over {q_{\rm e}}} = \displaystyle{{{\rm k}_2^{^\ast} t} \over {1 \!+ {\rm k}_2^{^\ast} t}}\,({\rm k}_2^{^\ast} = {\rm k}_2{q}_{\rm e})\,$$
$${\rm Elovich\ model} \quad \displaystyle{q \over q_{\rm e}} = \displaystyle{1 \over {\rm \beta} q_{\rm e}}\ln\! \lpar \alpha {\rm \beta} \rpar + \displaystyle{1 \over {\rm \beta} q_{\rm e}}lnt$$
$${\rm MPFO\ model} \quad \displaystyle{q \over {q_{\rm e}}} = {\rm ln}q_{\rm e}-k_{\rm m}t + \ln\! \lpar {q_{\rm e}-q} \rpar $$
$${{\rm Intra}-{\rm particle\ diffusion\ model}} \quad q_{\rm t} = {\rm k}_{\rm i}t^{1 / 2} + I$$
$${\rm IKL\ model} \quad \displaystyle{{q} \over {q_{\rm e}}} = \displaystyle{{\lpar {1 - {\rm e}^{-{\rm k}_{\rm L}t}} \rpar } \over {\lpar {1 - {\rm ae}^{-{\rm k}_{\rm L}t}} \rpar }}$$
$${\rm FLIKL\ model} \quad \displaystyle{q \over {q}_{\rm e}} = \displaystyle{{\lpar {1 - {\rm e}^{-{\rm k}_{\rm FL}t^{\rm n}}} \rpar } \over {\lpar {1 - f{\rm e}^{-{\rm k}_{\rm FL}t^{\rm n}}} \rpar }}$$where k1, k2, km, ki, kL and kFL are the rate constants of the pseudo-first-order, pseudo-second-order, MPFO, intra-particle diffusion, IKL and FLIKL models, respectively. In the Elovich equation, α is the adsorption rate at initial time and β is the correlation between the activation energy and the degree of surface coverage. In the IKL and FLIKL equations, a, n and f are constants.
Fig. 9. Adsorption kinetics of CV dye on zeolite-Mt (at initial dye concentrations of 20, 40, 60, 80 mg L−1 and optimum values of pH, temperature and zeolite-Mt dosage).
The kinetic parameters and the coefficient of determination (R2) obtained by fitting the kinetic data are listed in Table 7. The IKL and FLIKL models are suitable for fitting the experimental kinetic data because the predicted values agree well with the experimental values (R2 > 0.99). In addition, a comparison of the R2 values of the two kinetic models indicates that the FLIKL model with R2 = 0.9969–0.9989 fits the data better than the IKL model. Therefore, the zeolite-Mt surface is heterogeneous in accordance with the isotherm study.
Table 7. Kinetic parameters of CV adsorption on zeolite-Mt.
In similar kinetic studies of CV adsorption on raw and acid-treated Mt, the CV–clay mineral interactions were best described by a pseudo-second-order model, whereas the intra-particle and liquid film diffusion models were not suitable (Sarma et al., Reference Sarma, Sen Gupta and Bhattacharyya2016). In another study, the adsorption kinetics of CV adsorption onto magnetic zeolite nanoparticles was best fit by the pseudo-second-order kinetic model (Amodu et al., Reference Amodu, Ojumu, Ntwampe and Ayanda2015). The linear fitting of the kinetic data in these two studies is because of the linear increase of time over time and not because of the properties of the adsorption process (Simonin, Reference Simonin2016).
Thermodynamic studies
The thermodynamic parameters ∆G°, ∆H° and ∆S° may be determined from equations 19 and 20:
$$\Delta G^\circ = -{\rm R}T{\rm ln}{\rm K}_C$$
$${\rm ln}{\rm K}_C = -\displaystyle{{\Delta G^\circ} \over {{\rm R}T}} = \displaystyle{{\Delta S^\circ } \over {\rm R}}-\displaystyle{{\Delta H^\circ} \over {{\rm R}T}}$$where T is the solution temperature in Kelvin, KC is the dimensionless equilibrium constant and R is the gas constant (8.314 J mol−1 K−1). The KC constant was obtained as a dimensionless parameter by multiplying KL (Langmuir constant) by 55.5 and then 1000 (Tran et al., Reference Tran, You and Chao2016). The values of thermodynamic parameters were calculated from the plot of lnKCvs 1/T (Fig. 10). The results for the thermodynamic parameters are listed in Table 8. The positive ∆H° value of CV adsorption onto zeolite-Mt indicates that increasing adsorption of CV occurs with increasing temperature, which demonstrates that the adsorption is endothermic. The positive ∆S° value shows that disorder in the solution increases during adsorption, while the negative ∆G° value for adsorption onto zeolite-Mt suggests that the adsorption is spontaneous.
Fig. 10. Plot of lnKCvs 1/T.
Table 8. Thermodynamic parameters of CV adsorption on zeolite-Mt.
Conclusion
In this study, a zeolite-Mt nano-adsorbent composite was synthesized and tested in terms of its removal of CV dye from water. The effects of pH, temperature, initial dye concentration and adsorbent dosage were monitored during the adsorption process of CV onto zeolite-Mt. pH has the largest and most positive effect on dye-removal efficiency. Optimization for maximum removal of dye determined the required process conditions (temperature 25°C, pH 9, 2 g L−1 of adsorbent dosage and 40 mg L−1 initial dye concentration). The R2 value is close to unity and the experimental data agreed well with the predicted values, suggesting that the applied model is accurate. In the equilibrium study the Freundlich isotherm (R2 = 0.9936) and in kinetics study the FLIKL model (R2 > 0.99) fitted the adsorption best. The thermodynamic parameters showed that the CV adsorption onto the zeolite-Mt composite is an endothermic and spontaneous process. The comparison of q max for zeolite-Mt, Merck activated carbon and various adsorbents used in similar previous studies revealed that the zeolite-Mt nano-adsorbent is an affordable material with large adsorption capacity for removal of CV dye.
Acknowledgements
The authors are grateful to the University of Kashan for supporting this work through Grant No. 256750/4.
