Introduction

Water pollution is a global issue of great concern. Natural water bodies have continuously been polluted by human activities. Effluents from industries are one of the major water pollutants. Release of dye containing waste into water bodies affects both human and other living system of water bodies (Ambrosio et al. 2012). These industries include textiles, leather, plastic and paper. Removal of dye content from polluted water has become major part of water pollution research as most of these dyes are toxic (Gupta and Suhas 2009). Many research papers have been published on remediation of waste water containing dyes.

Various methods have been developed for the removal of dye from water, for example, photo degradation (Bansal et al. 2009) adsorption (Natali et al. 2011), coagulation, electro coagulation (Nandi and Patel 2013), electro dialysis (Amare et al. 2006), chemical oxidation (Baek et al. 2010a) and microbial degradation (Yang et al. 2016). All these methods have their own advantages and disadvantages, among which adsorption is a simple, rapid and effective method for this purpose. Many researchers have reported the use of different adsorbents for the removal of hazardous dyes from waste water, which includes, degreased coffee bean (Baek et al. 2010b), rice husk (Chowdhury et al. 2011), banana peel (Khalfaoui et al. 2012), root of water hyacinth (Wanyonyi et al. 2014), wood apple shell (Sartape et al. 2013), maize husk leaf (Jalil et al. 2012), potato plant waste (Gupta et al. 2016) and almond shell (Ozdes et al. 2010).

Methylene blue is a cationic dye having heterocyclic aromatic structure (Fig. 1). It is widely used in textile and paper industries. It can cause adverse health effects in humans, such as increased heart beat, cyanosis, jaundice, shock, tissue necrosis and vomiting (Sadhukhan et al. 2016). Hence, it is imperative to remove this dye from polluted water. In the present research work, we have used Saw palmetto spent as an efficient biosorbent for the removal of Methylene blue from aqueous solution.

Fig. 1
figure 1

Structure of Methylene blue

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Materials and methods

Adsorbate

Methylene blue used in this work was supplied by LobaChemie, Mumbai, India. Stock solution of 1000 ppm dye solution was prepared in double distilled water and test solutions of required concentration were prepared by diluting stock solution.

Adsorbent

Saw palmetto spent used in this work was supplied by nutraceutical industries (India). Industrially processed spent material was washed thoroughly with distilled water to remove chemical and physical impurities. The spent was ground to fine powder and again washed with distilled water and dried in sunlight. Dried spent powder is passed through sieves of different pore size and finally dried in hot air oven at 60 °C for 48 h and stored in air tight containers.

Characterization of Adsorbent

The scanning electron micrograph (SEM) of SPS was viewed under scanning electron microscope (Hitachi S3400N, Japan). FTIR (PerkinElmer-Spectrum two, USA) absorption spectra were obtained over the range 4000–600 cm−1.

Adsorption Studies

Batch adsorption experiments were carried out in 250 mL conical flask in an orbital shaker at 160 rpm. All experiments were conducted in triplicate and mean values are reported. The effect of initial dye concentration was carried out by varying the initial MB concentration from 10 to 100 mg L−1 using 0.05 g of adsorbent. Kinetic studies were carried out at three different initial dye concentrations 25, 50 and 100 mg L−1 using 0.05 g dose of spent. Batch adsorption experiments were carried out over a pH range of 4–11, to study the effect of pH on dye adsorption efficiency of SPS. The solution pH was measured by pH meter (Systronics 802). The pH of dye solution was adjusted using dilute HCl and NaOH. After the adsorption process, pH of the dye solution was adjusted back to pH 7 and measured using double beam UV–Vis spectrophotometer (Systronics 166) at the maximum absorption wavelength of MB (λ max = 618 nm). The adsorbed amount of MB at equilibrium q m (mg g−1) was calculated.

$$q_{\text{m}} = (C_{0} - C_{e} )V/W$$
(1)

where q m (mg g−1) is the equilibrium adsorption capacity, C0 and Ce are the initial and equilibrium concentrations (mg L−1) of MB dye solution, V is the volume (L) and W is the weight (g) of adsorbent.

Results and Discussion

Scanning Electron Microscopy (SEM)

The SEM images of SPS, before and after adsorption are shown in Figs. 2 and 3, respectively. Complexity in the morphology of SPS could be seen with porous structure.

Fig. 2
figure 2

SEM image of SPS before adsorption

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Fig. 3
figure 3

SEM image of SPS after adsorption

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After adsorption, there was appreciable change in the morphology of adsorbent which was marked by smoothness of surface due to adsorption of dye molecules, which fits into pores.

Fourier Transform Infrared Spectroscopy (FTIR)

FTIR spectra of SPS in the range of 600–4000 cm−1 (Fig. 4) have two main regions of absorption. The bands observed in the range of 1500–1600 cm−1 are due to the vibrations of aromatic rings present in the structure of lignin (Salazar-Rabago et al. 2016). A band at 3283 cm−1 is associated with stretching of hydroxyl groups of cellulose, hemicelluloses and lignin. A prominent absorption around 2925 cm−1 C–H stretching. Absorption band around 1628 cm−1 is due to carbonyl group stretching (Gupta et al. 2016). The bands of C–OH stretching were found at 1021 cm−1 (Hassan et al. 2013).

Fig. 4
figure 4

IR Spectra of SPS

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Effect of Initial Dye Concentration

The effect of initial concentration of MB on adsorption was studied over a range of 10–120 mg L−1. qm increased from 8 to 70 mg g−1 with the increase in MB concentration from 10 to 90 mg L−1. This may be due to increase in the driving force of the concentration gradient with the increase in the initial MB concentration (Hameed et al. 2008). When initial MB concentration was increased above 90 mg L−1, there was no significant change in the q m. This may be due to saturation of the adsorbent for further uptake of dye (Fig. 5).

Fig. 5
figure 5

Effect of initial dye concentration on qm

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Effect of pH

The pH is an important factor that affects adsorption, because surface charge of adsorbent is decided by pH of the solution. Adsorption experiments were performed to study the effect of pH with initial dye concentration of 90 mg L−1. From Fig. 6, it can be seen that q m increases from 68 to 71 mg g−1 when the pH was changed from 3 to 6, q m remains same in the pH range of 6–8, further increase in pH above 8, the q m gradually decreases and reaches 66 mg g−1 at pH 11. The solution pH not only changes the binding sites of the adsorbent but also affects aqueous chemistry. At lower pH the binding sites of adsorbent would be surrounded by H+ ions which competes with MB, hence q m decreases at lower pH (Khalfaoui et al. 2012). As the pH increases towards neutral, q m increases gradually reaches maximum at pH 6. When the pH was increased above 8, there was a slight depression in q m.

Fig. 6
figure 6

Effect of pH on q m

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Adsorption Isotherm

Isotherm studies are very important for better understanding of adsorption process. In the present research work commonly used Langmuir, Freundlich and Temkin isotherms were used to explain adsorbate–adsorbent interaction. Langmuir isotherm assumes monolayer adsorption on uniform surface containing limited numbers of sites (Gupta et al. 2016).

The linear form of Langmuir isotherm is given by the following equation;

$$C_{e} /q_{\text{m}} = \left( {1/bq_{\text{m}} } \right) + \left( {1/q_{\text{m}} } \right) \, C_{e}$$
(2)

where, q m is the amount of dye adsorbed per unit mass of adsorbent (mg g−1), C e is the equilibrium concentration of the adsorbate in solution (mg L−1), Langmuir constant q m is the monolayer sorption capacity and constant b is related to adsorption energy (L mg−1). q m and b can be calculated from the slope and intercept of the linear plot of C e/q m versus C e.

Freundlich isotherm model is an empirical equation that describes adsorption process based on a heterogeneous surface. Linear form of the Freundlich expression is presented below:

$$\log \, q_{\text{m}} = \log \, K_{\text{f}} + n_{\text{f}} \log \, C_{e}$$
(3)

The Freundlich constants K f and n f are calculated from the plots of log q m versus log C e. K f is related to adsorption capacity and n f is the measure of adsorption intensity or surface heterogeneity. The value of n f ranges between 0 and 1, the adsorption becomes more heterogeneous as its value gets closer to zero (Akar et al. 2013).

Temkin describes the process by considering some indirect adsorbate–adsorbate interactions on adsorption (Khalfaoui et al. 2012). This isotherm explains that the linear decrease in heat of adsorption of all the molecules in the layer is the impact of these interactions. The linear form of Temkin isotherm is:

$$q_{\text{m}} = \frac{RT}{b}\ln A \, + \frac{RT}{b}\ln C_{e} \quad {\rm with} B= \frac{\text{RT}}{\text{b}},$$
(4)

where, q m is adsorption capacity (mg g−1), C e is equilibrium dye concentration (mg L−1). A and B are Temkin constants, related to equilibrium binding constant (L g−1) and heat of adsorption (J mol−1) respectively. The values of A and B can be calculated from intercept and slope of the linear plot q m versus lnC e. The values of constants and correlation coefficients of all the three isotherm models are presented in Table 1.

Table 1 Adsorption Isotherm constants and correlation coefficients
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The data obtained experimentally found to fit best with Langmuir isotherm model with regression coefficient (R 2) 0.959, which indicates the monolayer adsorption of MB onto SPS. The value of R L (0.0833) also supports the feasibility of this isotherm, which describes the shape of isotherm to be either favourable (0 < R L < 1), unfavourable (R L > 1) or irreversible (R L = 0) (Sartape et al. 2013) (Figs. 7, 8, 9).

Fig. 7
figure 7

Langmuir adsorption isotherm

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Fig. 8
figure 8

Freundlich adsorption isotherm

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Fig. 9
figure 9

Temkin adsorption isotherm

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Kinetic Studies

Study of kinetics is an important part of adsorption research as it explains the rate of adsorption from which mechanism of the process and rate controlling steps could be predicted.

In the present study, we used three different models to predict the adsorption kinetics of MB onto SPS (Pseudo-first order, Pseudo-second order and Intra-particle diffusion model).

Pseudo-First Order Kinetic Model

The differential rate equation is

$${\text{d}}q_{\text{t}} /{\text{d}}t = k_{1} (q_{\text{m}} - q_{t} )$$
(5)

where, k 1 is the pseudo-first order rate constant (min−1), qt and q m are the amount of dye adsorbed at any time t (mg g−1) and at equilibrium (mg g−1), respectively. Integrating Eq. (5) using the boundary condition, q t = 0 at t = 0 leads to:

$$\log \, (q_{\text{m}} - q_{\text{t}} ) = \log q_{\text{m}} - \left( {k_{1} /2.303} \right)t$$
(6)

The values of k 1 and q m were calculated from the slope and intercept of the linear plots of log (q m − q t) versus t (Fig. 10) respectively, and presented in Table 2.

Fig. 10
figure 10

Pseudo-first order kinetic model

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Table 2 Kinetic parameters
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Pseudo-Second Order Kinetic Model

The pseudo-second order kinetic model can be presented as:

$${\text{d}}q_{\text{t}} /{\text{d}}t = k_{2} (q_{\text{m}} - q_{t} )^{2}$$
(7)

where, k 2 is the pseudo-second order rate constant (g mg−1 min−1), q t and q m are the amount of dye adsorbed at any time t (mg g−1) and at equilibrium (mg g−1), respectively. Integrating Eq. (7) using the boundary condition, q t = 0 at t = 0 leads to:

$$t/q_{\text{t}} = 1/k_{2} q_{\text{m}}^{2} + t/q_{\text{m}}$$
(8)

The values of k 2 and q m were calculated from intercept and slope of the linear plots of t/q t versus t (Fig. 11), respectively, and presented in Table 2.

Fig. 11
figure 11

Pseudo-second order kinetic model

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Intra-Particle Diffusion Model

This model was developed by Weber and Morriss (1963), which can be expressed as:

$$q_{t} = K_{i} t^{1/2} + C$$
(9)

where, K i is intra-particle diffusion rate constant (mg g−1 min1/2) and C is the measure of boundary layer effect. Values of K i and C were obtained from slope and intercept of the plots of q t versus t 1/2. According to this model, the plots of q t versus t 1/2 should be linear if intraparticle diffusion is involved in the adsorption process and diffusion is rate controlling step if the line passes through origin (Chen et al. 2003) (Fig. 12).

Fig. 12
figure 12

Intra-particle diffusion model

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The parameter of all the models used to study the kinetics of the adsorption models are summarized in the Table 2. The plot of intraparticle diffusion model did not pass through origin, which indicates, intraparticle diffusion is not only the rate limiting step, and some other factors may also control the rate of adsorption, which may be operating simultaneously (Yakout and Elsherif 2010). The correlation coefficients R 2, for the pseudo-first-order model (R 2 0.987–0.996) were greater than that of the intraparticle diffusion coefficients (R 2 0.882–0.981), which suggest chemisorption mechanism (Ho and McKay 1998).

Effect of Temperature

The adsorption studies were carried out at 303, 313, 323 and 333 K and the results are shown in Fig. 13. It was observed that as the temperature increased the adsorption capacity also increased, which indicates that the process is endothermic in nature. Change in temperature of the system may alter the porosity of the biosorbent due to swelling, which may influence the intraparticle movement of dye molecules (Singh and Srivastava 1999; Aksu et al. 2008).

Fig. 13
figure 13

Effect of temperature on q m

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Thermodynamics of Adsorption

The important factors that are to be considered in the adsorption process are energy and entropy. The standard Gibbs free energy change (∆G°) is the measure of spontaneity of the adsorption process. Significant adsorption occurs, when the ∆G° of adsorption becomes negative, and these thermodynamic parameters were calculated using van’t Hoff and Gibbs–Helmholtz equations.

$$K_{L} = \frac{{C_{0} }}{{C_{e} }}$$
(10)
$$\Delta G^\circ = - RT\ln K_{L}$$
(11)
$$\ln K_{L} = {\raise0.7ex\hbox{${\Delta S^\circ }$} \!\mathord{\left/ {\vphantom {{\Delta S^\circ } R}}\right.\kern-0pt} \!\lower0.7ex\hbox{$R$}} - {\raise0.7ex\hbox{${\Delta H^\circ }$} \!\mathord{\left/ {\vphantom {{\Delta H^\circ } {RT}}}\right.\kern-0pt} \!\lower0.7ex\hbox{${RT}$}}$$
(12)

where, C 0 and C e are the initial and equilibrium concentration (mg L−1) of dye in solution, T is the temperature (K) and K L is the Langmuir equilibrium constant (L mol−1). The value of ΔG0 was calculated from Eq. (11), the slope and intercept of the van’t Hoff plots of ln(K L) versus 1/T were used to determine the values of ΔH 0 and ΔS 0 (Table 3; Fig. 14).

Table 3 Thermodynamic parameters
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Fig. 14
figure 14

van’t Hoff plot for the adsorption of MB onto SPS

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Positive value of ΔH 0 (70.96 kJ mol−1) indicates that, the adsorption process is endothermic, and the possibility of physical adsorption (Hema and Arivoli 2008). Negative values of ΔG 0 indicates that adsorption of MB onto SPS is favourable and spontaneous. Adsorption process was more spontaneous at higher temperature as the ΔG 0 values decreased from −31.18 to −41.91 kJ mol−1 with increasing temperature (Chu et al. 2004). Increase in randomness at the solid–solution interface was indicated by the positive value of ΔS 0 (0.34 kJ mol−1) (Namasivayam and Kavitha 2002).

Conclusion

In the present study, SPS has been successfully used as an economical and eco-friendly adsorbent for the remediation of a toxic dye, Methylene Blue from its aqueous solution. Maximum adsorption capacity was q m = 71.00 mg g−1, and it is near to the value obtained by Langmuir (q m = 90.9 mg g−1) with correlation coefficient (R 2) of 0.959. The kinetic data obtained experimentally, better fits with pseudo-first order kinetic model. Possible mechanisms of MB–SPS interactions that can occur in system have been discussed. From thermodynamic parameters it is clear that the adsorption process is endothermic and spontaneous. Finally it could be concluded that, SPS can be used as a fast and effective biosorbent for the removal of MB from aqueous solutions.