Iron-activated carbon nanocomposite: synthesis, characterization and application for lead removal from aqueous solution

Abstract

The removal of Pb(II) ions from aqueous solution by adsorption on an Iron-Activated Carbon (IAC) nanocomposite was investigated. Removal studies were carried out in a batch system, and the effects of various operating parameters, such as solution pH, solid to liquid ratio and initial concentration were evaluated. Experimental design was carried out using central composite design (CCD) with response surface methodology (RSM). According to the RSM results, the optimum adsorption conditions for Pb(II) removal by IAC were pH = 6.5, solid to liquid ratio of 3 g L⁻¹ and initial lead concentration of 10 mg L⁻¹. Under these optimum operating conditions, 96.5% of Pb(II) was removed by the IAC nanocomposite. The equilibrium adsorption data were well described by the Freundlich isotherm. The maximum adsorption capacity of IAC was 121.9 mg g⁻¹ for Pb(II). It was observed that the adsorption kinetics of Pb(II) on the IAC could be well analyzed with a pseudo-second-order model.

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Introduction

Heavy metal contamination of water resources is a major source of concern because of its toxic effect on human health and on animals and plants in the environment. Lead is a heavy metal that is released to the environment through a number of industrial activities such as battery production, coating and painting industries, pulp and paper production, petrochemical industries, etc.^1–6 The presence of lead in the environment, even at very low concentrations, is extremely harmful to the aquatic environment and human health. Lead can damage the nervous system, kidneys, reproductive system and other organs.⁷ The World Health Organization (WHO) and US Environmental Protection Agency (US EPA) have set a maximum guideline concentration of 0.01 and 0.015 mg L⁻¹ for Pb in drinking water, respectively.⁸

Several separation methods, such as chemical precipitation, electrocoagulation, ion exchange, membrane processes and adsorption have been suggested for heavy metal removal from industrial wastewater to decrease their impact on the environment.^9–16 Among these, adsorption seems to be the most suitable technique for the removal of metals in low concentration, due to low cost and high efficiency.

Activated carbon has been chosen as an adsorbent for removal of different pollutants from liquid and gas systems due to its high adsorption capacity, high adsorption rate and good resistance to abrasion.^17–20 However, its ability to adsorb inorganic ions is relatively low.^21–24 To increase the adsorption capacity or making the process more economical, modification of the adsorbent has been proposed. Deposition of iron nanoparticles on activated carbon is a novel and promising way to modify activated carbon and overcome these limitations.

Iron-activated carbon (IAC) nanocomposite have been synthesized via anchorage of iron oxide–hydroxide nanoparticles onto the activated carbon surface. Different techniques have been tested with this aim, such as iron precipitation with alkaline solutions, evaporation of iron salt solution in the presence of activated carbon, and oxidation/precipitation of iron.^25–27 However, since a large amount of energy is consumed with some techniques,²⁴ synthesis of IAC by evaporation of iron salt solution appears the optimal one.^22,28

In this work a IAC nanocomposite was synthesized and characterized using different techniques. The main objectives of the present study include the following:

(1) To present a facile, low temperature synthesis technique for the preparation of IAC nanocomposite with good adsorption properties.

(2) To investigate the performance of the synthesized IAC for Pb(II) removal from aqueous solutions under different operating conditions (pH, solid to liquid ratio, lead initial concentration and temperature).

(3) To analyze the kinetic and equilibrium data in order to better clarify the lead adsorption mechanisms on IAC.

Materials and methods

Materials

Commercial Norit ROY 0.8 activated carbon (AC) was used as template. Nitric acid (Merck Co., 63%) and Fe(NO₃)₃ (Merck Co.) were used to synthesize iron-activated carbon (IAC) nanocomposite. Pb(NO₃)₂ (Sigma-Aldrich Company) and deionized water were used to prepare the test solutions. The initial pH of the solution was adjusted by HCl and NaOH solutions (Sigma-Aldrich Co.).

Preparation and characterization of adsorbent

Preparation of IAC nanocomposite. To synthesize IAC, 10 g of AC sample was mixed with 150 mL of nitric acid for 3 h at 45 °C. The nitric acid pretreated AC was repeatedly rinsed with deionized water until the pH of washing water become constant. Next, 10 g of nitric acid pretreated AC was contacted with 200 mL of 0.6 M Fe(NO₃)₃ solution inside a sealed glass container. The sample was placed in an agitating incubator (NB-205, N-BIOTECK) at 55 °C for 24 h. Finally, the mixture was filtered with paper filter (Whatman no. 42) and IAC nanocomposite was rinsed with deionized water until all soluble iron and iron particles that were not anchored to surface of AC, were removed. To ensure separation of all soluble iron, the rinsing water was analyzed for iron using inductively coupled plasma (ICP) emission spectrometry (Perkin Elmer Optima 2100 DV). After washing, the sample was dried at 60 °C in an air oven overnight. The dried adsorbent was kept in a desiccator.^22,26

Characterization of adsorbent. IAC adsorbent was characterized by selected physical and chemical properties:

• The pH_ZPC (point of zero charge) of sample was measured by the following method: 0.3 g of adsorbent was added to 50 mL of a 0.1 N NaCl solution, where the initial solution pH was adjusted using aqueous HCl or NaOH solutions. If the initial pH of the solution is equal to the pH_ZPC of the adsorbent, no change in the pH will be observed after adding adsorbent to the solution.²⁹

• The porous texture and surface area of samples (AC and IAC) were determined from adsorption–desorption of N₂ at 77 K using an automatic volumetric system (Quantachrome NOVA 1000). The surface area was calculated by applying the BET (Brunauer–Emmett–Teller) method. The volume (mL g⁻¹) and width (Å) of micropore and mesopore were determined by HK (Horvath–Kawazoe) and BJH (Barrett, Joyner, and Halenda) methods, respectively.^30,31 The total pore volume of the sample was determined from the amount of nitrogen adsorbed at the highest relative pressure examined.

• The surface chemistry of AC and IAC samples was determined by using Fourier transform infrared radiation (Nicolet FT-IR spectrophotometer, NEXUS 670, 4 cm⁻¹ resolution, sample/KBr = 1/100) within the range of 400–4000 cm⁻¹ wave number.

• The iron content of AC and IAC nanocomposite was determined by using X-ray fluorescence (XRF, Unisantis, XMF-104).

• The average particle size, the morphology of AC and IAC nanocomposite and the composition of IAC nanocomposite were determined by scanning electron micrography (SEM-EDS, Tescan).

• X-ray diffraction (XRD) spectra of AC and IAC nanocomposite were obtained using a high-resolution X-ray diffractometer (Philips X'Pert).

Effect of adsorption parameters

In this study the effects of three removal parameters such as solution pH, solid to liquid ratio and initial concentration were investigated. Lead removal experiments were performed in batch manner by adding a measured weight of IAC in 50 mL of Pb(II) solutions with a fixed initial concentration and pH in 100 mL Erlenmeyer flasks. These samples were shaken at 25 °C using an orbital shaker (FINEPCR-SH30) at 150 rpm for 6 h. At the end of the experiment, the adsorbent was separated by paper filter (Whatman no. 42), and the residual Pb(II) concentration in the solution was determined using ICP emission spectrometry. The removal percentage of Pb(II) was determined from eqn (1):in which C₀ and C_f are the initial and final metal ion concentration (mg L⁻¹), respectively.

Design of experiments. In this work, pH of solution, solid to liquid ratio and initial concentration of lead were chosen as independent parameters. Since the number of independent parameters is three, according to CCD method 20 experiments consisting of 8 factorial points, 6 axial points and 6 replicates at the center points were carried out.^32–36

Kinetic studies

Adsorption kinetics were studied by placing 0.05 g of adsorbent in flasks containing 50 mL of 100 mg L⁻¹ Pb(II) solution at pH 6.0. The flasks were agitated in an orbital shaker at 150 rpm for prescribed periods of time (2–360 min) at 25 °C. After these periods of time, solid and liquid were separated by filtration, and the solution concentration was analyzed using ICP emission spectrometry. The metal uptake capacity, q, was calculated as:where q, V and m are the amount of solute adsorbed per unit weight of adsorbent (mg g⁻¹), solution volume (L) and dry weight of adsorbent (g), respectively.

Kinetic models. The pseudo-first-order, pseudo-second-order, Weber–Morris and Boyd kinetic models were used for analyzing the kinetic data and therefore to evaluate the kinetic mechanism that controls the adsorption process. The pseudo-first-order and pseudo-second-order kinetic models assume that the limiting step of the process is the adsorption pseudo-reaction, and that this reaction has a first or a second order kinetic, respectively. If the reaction follows a first order equation, kinetic data can be described by the following equation:

ln(q_e − q_t) = ln q_e − k₁t (3)

where q_e is the amount of solute adsorbed at equilibrium (mg g⁻¹) and k₁ (min⁻¹) is the pseudo-first-order rate kinetic constant, that can be calculated by plotting ln(q_e − q_t) vs. t.

On the other hand, the pseudo-second-order rate equation can be written as:

where k₂ (g mg⁻¹ min⁻¹) is the pseudo-second-order rate constant, which can be evaluated by plotting t/q_t vs. t.

Weber and Morris found that in many adsorption processes the initial solute uptake is almost proportional to t^1/2, and interpreted this result by stating that the limiting step for the process is intra-particle diffusion.³⁷ Their model can be described by the following equation:

q_t = k_idt^0.5 (5)

in which k_id (mg g⁻¹ min^−0.5) is the internal mass transfer coefficient, that can be determined from a plot of q_t vs. t^0.5. If plot of q_t against t^0.5 is linear and passes through the origin, then intra-particle diffusion will be the only rate controlling mechanism. Otherwise, it can be assumed that other mechanisms play a role together with intra-particle diffusion, and the intercept gives an idea about the thickness of the diffusional boundary layer, in the sense that a thicker boundary layer leads to a larger intercept.³⁵

Eventually, the last model which was considered has been the Boyd model. This model too can be used to estimate the role of intra-particle diffusion with respect to film diffusion:³⁸

Bt = −0.4977 − ln(1 − F) (6)

in which F is equal to q/q_e and B can be used for determination the effective diffusion coefficient. A plot of [−0.4977 − ln(1 − F)] against t can be employed to test the model: if the plot is linear and passes through the origin, then it can be assumed that the rate controlling step in the adsorption process is internal diffusion; otherwise, if the plot is nonlinear or linear but does not pass though the origin, this process may be controlled by external diffusion.

Equilibrium studies

The equilibrium of Pb(II) adsorption by IAC nanocomposite was studied by placing 0.05 g of adsorbent in a series of flasks containing 50 mL of Pb(NO₃)₂ solution at different initial concentrations (10–220 mg L⁻¹ of Pb(II)) and with an initial pH of 6.0. The flasks were agitated in an orbital shaker at 150 rpm for 24 h at various temperatures (25°, 45° and 60 °C). After this period, the metal solution was filtered and the residual concentration of the metal ions was determined. The amount of lead adsorbed by adsorbent, q_e, was determined using eqn (2).

Adsorption isotherms. The three most widely used adsorption isotherms are the Langmuir, Freundlich and Dubinin–Radushkevich (D–R) isotherms. In this work, experimental data was analyzed in terms of these isotherms. The Langmuir adsorption isotherm equation in linear form is:where, q_max is maximum adsorption capacity of adsorbent (mg g⁻¹) and K_L is the Langmuir constant (L mg⁻¹), which is related to energy of adsorption. Another important parameter, R_L, called the separation factor, can be calculated to identify whether an adsorption system is favorable or unfavorable:

If the R_L value is between 0 and 1, the adsorption process is favorable.

The linear form of Freundlich model is:

in this equation, K_f is the Freundlich constant, which is a measure of the adsorption capacity of adsorbent (mg^1−(1/n) L^1/n g⁻¹), and 1/n is the Freundlich exponent, which is related to adsorption intensity.

The Dubinin–Radushkevich (D–R) isotherm was applied since it allows to evaluate if the nature of adsorption as physical or chemical. The linear form of Dubinin–Raduskevich isotherm is:

In this equation, q_m is the theoretical saturation capacity (mol g⁻¹), ε is the Polanyi potential, which is related to the equilibrium concentration as follows:

where R is the universal gas constant (8.314 J mol⁻¹ K⁻¹), T (K) is the absolute temperature, and E (kJ mol⁻¹) is the mean free energy of adsorption, i.e. the energy necessary to transfer one mole of adsorbate to the surface of the solid from infinity in the solution. In practice E can be used to discriminate between physical adsorption (typically E < 8 kJ mol⁻¹) and a chemisorption/ion exchange (typically 8 < E < 16 kJ mol⁻¹).¹⁵

Results and discussion

Characterization of the adsorbents

Chemical properties. The iron contents of AC and IAC nanocomposite were <0.2% and 7.78% wt, respectively: this difference indicates that iron coated AC efficiently. The crystalline structure of iron nanoparticles on AC surface was verified by XRD patterns shown in Fig. S1.† This figure shows nanocrystalline peaks well matching with the ICDD reference phase no. 00-003-0251, suggesting that iron formed iron oxide–hydroxide (FeOOH) nanoparticles.³⁹ The pH_ZPC of AC and IAC samples were 6.1 and 4.8 respectively. The decrease of pH_ZPC is attributed to the formation of iron oxide–hydroxide nanoparticles on the surface of AC.

Porous texture of the adsorbents. Nitrogen adsorption–desorption isotherms for the samples are presented in Fig. 1. The results show that the adsorption–desorption isotherms of the samples are the combination of type I and type II in IUPAC classification.^40,41 These types of isotherms can be observed in porous materials with a combination of micro and mesopores. The small hysteresis loops of the isotherms are H4 class, which are often associated with narrow slit-like pores with a regular structure.⁴²

Fig. 1 N₂ adsorption–desorption isotherms for AC and IAC nanocomposite.

The porosity parameters of both raw AC and IAC nanocomposite have been derived from N₂ adsorption–desorption data (Table 1). It can be seen that the formation of iron nanoparticles inside and outside the porous structure of the AC causes a slight reduction in BET surface area and pore volume (both micro and mesopore), and a small increase in the average pore width, again both in the micro and meso ranges. As pointed out by Arcibar-Orozco et al.,²⁸ the extent of BET area reduction can be taken as an indication of the fact that iron oxide–hydroxide nanoparticles, with sizes in the order of a few tens of nm, were formed. Indeed, large particles deposited on the AC surface would have blocked the pore entrances, leading to a significant reduction in N₂ adsorption capacity. In turn, the formation of iron oxide–hydroxide nanoparticles is quite important, since the AC pores remain accessible, and the interaction between physical adsorption and chemisorption can enhance the separation process.

Table 1 Porosity parameters of AC and IAC nanocomposite

Parameter	AC	IAC
BET Sp. Surf. area (m^2 g^−1)	1062	890
Total pore volume (mL g^−1)	0.66	0.545
Micropore volume (mL g^−1), HK method	0.53	0.47
Mesopore volume (mL g^−1), BJH method	0.13	0.080
Volume occupied by micropores (%)	80.0	86.2
Average micropore width (Å), HK method	15.5	16.7
Average mesopore width (Å), BJH method	22.5	26.4

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Surface functional groups. The FT-IR spectra of the AC and IAC samples are showed in Fig. 2. The bands at 3434 cm⁻¹ can be attributed to the absorption of water molecules due to the stretching of OH.⁴³ For both samples, the peaks at 2930 cm⁻¹ are attributed to C–H aliphatic stretching.⁴³ The bands at 2850 cm⁻¹ can be attributed to dimer of OH in carboxylic acid.⁴³ In the region 1300–1750 cm⁻¹, amides can be distinguished on surface of samples.⁴² The bands at 1117 cm⁻¹ can be attributed to the stretching of C–O in carboxylic acid.⁴³ The comparison between the spectra of IAC and AC indicates that functionalization with iron leads to new peaks at 882 cm⁻¹, 790 cm⁻¹ and 638 cm⁻¹. These new peaks confirm the formation of Fe–OH, Fe–O and C–O–Fe bonds.^44,45

Fig. 2 FT-IR spectra AC and IAC nanocomposite.

Structure and morphology of adsorbents. Fig. 3a and b show the SEM micrographs of AC and IAC nanocomposite, respectively. The SEM micrograph of IAC reveals the presence of iron oxide–hydroxide nanoparticles inside the pores and on the particle surface. The oxide–hydroxide nanoparticles have “slivered almonds” shape and are uniformly distributed on the AC surface. The size of these nanoparticles ranges from a width of 70 nm to 120 nm, with a length of less than 500 nm. Furthermore, Fig. 3c and d show the EDS and element mapping for IAC. EDS diagram indicates the presence of iron on the surface of nanocomposite, and the element mapping figure shows that the distribution of iron on IAC surface is good.

Fig. 3 SEM micrograph of (a) AC, and (b) IAC nanocomposite; (c) EDS analysis of IAC; (d) element mapping of IAC nanocomposite.

Effect of separation parameters

Regression model equation development. The complete design matrix and the response values obtained from the experimental works have been given in Table S1.† Eqn (12) represents the empirical quadratic model that was used to fit the experimental data, with the coefficients of this model obtained by multiple regression analysis technique using DESIGN-EXPERT 7.0 software:

Removal percentage (%) = −20.02 + 13.90(X₁) − 9.38(X₂) + 0.44(X₃) − 1.21(X₁X₂) − 0.12(X₁X₃) − 0.01(X₂X₃) + 0.75(X₁²) + 5.42(X₂²) + 1.65 × 10⁻⁴(X₃²) (12)

In this equation X₁, X₂ and X₃ are solution pH, solid to liquid ratio (g L⁻¹) and initial concentration of Pb(II) (mg L⁻¹), respectively.

Eqn (12) shows that removal of Pb(II) is linear with respect to pH, solid to liquid ratio and initial concentration, and quadratic with respect to solid to liquid ratio and pH. This equation also indicates that there are interactions between pH and solid to liquid ratio and between pH and initial concentration on Pb(II) removal. The analysis of variance was applied in order to ensure a desirable model. The ANOVA results are shown in Table 2. According to the rule, the best regression model is determined by highest Fisher’s F value and lowest p-value.^36,46 Data in Table 2 indicate that the quadratic model was significant at the 95% confidence level. As it can be seen from inspection of Table 2, F value for the model is 98.74 with corresponding P < 0.0001 and high SS (9264.55). This implies that the model is significant, and can appropriately explain the relationship between response and independent variables. This conclusion is also confirmed by the fact that both R² and R_adj² approached unity.

Table 2 ANOVA table for lead removal. R² = 0.9889; R_adj² = 0.9789

Source	Sum of squares	df	Mean square	F value	p-Value
Model	9264.55	9	1029.39	98.74	<10^−4
X1	7001.29	1	7001.29	671.53	<10^−5
X2	218.27	1	218.27	20.94	0.0010
X3	34.21	1	34.21	3.28	0.1002
X1X2	92.96	1	92.96	8.92	0.0137
X1X3	1245.25	1	1245.25	119.44	<10^−4
X2X3	5.27	1	5.27	0.50	0.4936
X1^2	55.95	1	55.95	5.37	0.0430
X2^2	277.12	1	277.12	26.58	0.0004
X3^2	0.66	1	0.66	0.063	0.8062
Residual	104.26	10	10.42
Pure error	2.58	5	0.52

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​p-Values that are lower than 0.05 allow to individuate the model terms which are significant, while values greater than 0.1 allow to individuate model terms are not significant, and can be omitted.⁴⁷ Based on ANOVA table, the linear effects of pH (X₁) and solid to liquid ratio (X₂) and quadratic effects of solid to liquid ratio (X₂²) and pH (X₁²) are more significant. Table 2 also indicates that interactive effects between pH and solid to liquid ratio (X₁X₂) and pH and initial concentration (X₁X₃) have a significant influence on Pb(II) removal. Eliminating the less significant terms from eqn (12) and refining the model, eqn (12) may be simplified to:

Removal percentage (%) = −20.02 + 13.90(X₁) − 9.38(X₂) − 1.21(X₁X₂) − 0.12(X₁X₃) + 0.75(X₁²) + 5.42(X₂²) (13)

It can be concluded that through response surface methodology it is possible to individuate a reasonable model for Pb(II) removal process, which can be used for prediction of removal efficiency. By applying the diagnostic plots, such as the predicted vs. actual value plots, the models adequacy can be assessed to verify if the selected models provide adequate approximation of the real system. Fig. 4 shows the relationship between predicted and actual values of Pb(II) removal using IAC.

Fig. 4 Predicted vs. actual values plot for lead removal using IAC nanocomposite.

It is clear that the predicted values are quite close to the actual experiments, which suggests that the model is accurate.

The perturbation plot (Fig. S2†) shows the comparative effects of all independent variables on Pb(II) removal efficiency. In Fig. S2† curvature in pH is the sharpest and effect of solid to liquid ratio is sharper than initial concentration: this indicates that Pb(II) removal efficiency is very sensitive to pH compared to solid to liquid ratio and initial concentration. This can be realized from ANOVA table as well. As it was indicated in Table 2, the F value of pH is higher than those relative to solid to liquid ratio and initial concentration.

Response surface plots. Three dimensional (3D) response surface plots were generated to investigate the effects of the three process parameters considered, i.e. solution pH, solid to liquid ratio and initial concentration on lead removal (Fig. 5).

Fig. 5 Response surfaces plots for lead removal using IAC. (a): Effect of solution pH (X₁) and solid to liquid ratio (g L⁻¹, X₂); (b): effect of solution pH (X₁) and initial concentration of Pb(II) (mg L⁻¹, X₃); (c): effect of solid to liquid ratio (g L⁻¹, X₂) and initial concentration of Pb(II) (mg L⁻¹, X₃).

Fig. 5a and b show the effect of solution pH on the removal efficiency. According to these figures, the removal efficiency of lead increased with pH ranging from 2 to 6.5 at any fixed initial concentration and solid to liquid ratio, even though this effect is smaller at higher initial concentrations. This trend may be the result of interaction of pH and initial concentration. Presumably the fact that the solution pH is one of the most influencing parameters of the lead removal process derives from it affecting the surface functional groups and the speciation of lead in solution. Indeed, the fact that pH_ZPC was 4.8 for IAC means that the surface charge of the adsorbent is positive and negative when pH < 4.8 and pH > 4.8, respectively. Since the dominant forms of lead at initial pH < 6–6.5 is Pb²⁺ cations,^48,49 at pH < pH_ZPC, the surface of IAC is positive and electrostatic repulsion occurs between Pb²⁺ cations and positive adsorption sites of IAC, while at pH > pH_ZPC, the adsorbent surface is negative and it can more easily adsorb Pb²⁺.

According to Fig. 5a and b, the removal efficiency first decreased with increasing adsorbent dosage, and then increased. The presence of a minimum is quite difficult to address: it can be in principle due to some sort of interaction between the solution pH and the solid to liquid ratio, due to a not neutral hydrolysis of the adsorbent. Nevertheless, further investigation is needed in order to give a proper explanation of this particular response.

Adsorption kinetics

Fig. 6 reports an example of kinetic experiment for lead uptake by IAC nanocomposite. The other experiments gave similar results, so are not showed in this paper. The results demonstrate that the separation rate is fast for the first 5 min and then decreases, with the adsorption process reaching equilibrium at about 200 min. As mentioned above, the experimental data were analyzed in terms of pseudo-first-order, pseudo-second-order, intra-particle diffusion and Boyd kinetic models. The constants and regression coefficient (R²) for these models are presented in Table S2.† The R² values indicate that the pseudo-second-order model is more appropriate than the pseudo-first-order model for description of the lead removal process. Also, from the table it appears that the calculated q_e from the pseudo-second-order model is very closely similar to the experimental value of q_e. These results confirm the validity of this model to the lead removal kinetics. This suggests that, under the experimental conditions taken into account, the lead removal by IAC is controlled by chemical sorption.

Fig. 6 Effect of time (min) on lead removal using IAC nanocomposite.

According to data presented in Table S2,† the R² value for the Weber–Morris model is 0.95; however, despite the high value of R², it has to be observed that the intercept of the straight line relative to the plot of q_t vs. t^0.5 is significantly different from zero, suggesting that intra-particle diffusion is not the only rate controlling process.

A similar conclusion can be reached for what concerns the Boyd model: despite the fact that the R² value is quite high (>0.97), the plots of −0.4977 − ln(1 − F) vs. t have an intercept significantly different from zero. Based on these results, the film diffusion may be important in lead adsorption by IAC. The B value was used to calculate the effective diffusion coefficient, D_i (m² s⁻¹) using the following equation:

from which it is obtained D_i = 1.53 × 10⁻¹¹ m² s⁻¹.

Adsorption equilibrium

Fig. 7a–c illustrate the predicted equilibrium adsorption values vs. equilibrium concentration at different temperatures using Langmuir, Freundlich and Dubinin–Raduskevich (D–R) isotherm models. Constant parameters and regression coefficient (R²) of these isotherms are presented in Table 3. Results show that Freundlich isotherm best fits the equilibrium data at all the temperatures. This may be attributed to the coexistence of different sorption sites and/or different sorption mechanisms or the sorption of different lead species that have led to a heterogeneous adsorption. The maximum adsorption capacity of IAC nanocomposite was 121.9 mg g⁻¹. The maximum adsorption capacities reported for various adsorbents for lead are shown in Table 4. The comparison indicates that IAC nanocomposite exhibits a reasonably high capacity for lead adsorption from aqueous solutions.

Fig. 7 Adsorption isotherms of lead on IAC nanocomposite at different temperatures: (a) 25 °C, (b) 45 °C, (c) 60 °C.

Table 3 Equilibrium isotherm parameters for lead removal on IAC nanocomposite

Isotherm	Parameters	25 °C	45 °C	60 °C
Langmuir	KL (L mg^−1)	0.3873	0.04703	0.0312
	qmax (mg g^−1)	121.9	87.71	44.05
	R^2	0.976	0.967	0.968
	RL	0.011–0.205	0.088–0.680	0.127–0.762
Freundlich	Kf	10.67	5.40	3.929
	N	1.21	1.58	2.976
	R^2	0.993	0.991	0.989
D–R	qm (mol g^−1)	0.0071	0.00289	0.0006
	R^2	0.971	0.968	0.972
	E (kJ mol^−1)	9.218	10.00	15.811

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Table 4 Adsorption capacities of various adsorbents for lead

Adsorbent	Adsorption capacity (mg g^−1)	Ref.
Nanometer titanium dioxide immobilized on silica gel	3.16	48
Activated carbon	147.06	49
Activated carbon	51.81	50
Pine cone activated carbon	27.53	51
Fe3O4/SiO2 nanocomposite	17.65	52
Chemically treated pulp (1 M citric acid)	34.6	53
EDTA modified activated carbon	60.03	54
Activated carbon	338	55
Fe3O4@C	71.42	56
Fe3O4 nanospheres	18.47	57
(α,γ-FeOOH) nanoparticles	820	58
Co0.6Fe2.4O4 micro-particles	80.32	59
MHC/OMCNTs	116.3	60
IAC nanocomposite	121.9	This work

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The R_L values for lead removal indicate that the adsorption isotherms are favorable at all temperatures considered. From the D–R isotherm parameters it is obtained that the mean free energy (E) of adsorption per molecule of the adsorbate was in the range of 9.22–15.8 kJ mol⁻¹, suggesting that the process under consideration has a markedly chemical nature (chemisorption), rather than being a pure physical adsorption: this is in agreement with the results of kinetic studies, which indicate that the controlling step for the adsorption process is the adsorption pseudo-reaction.

Thermodynamic parameters

Inspection of Fig. 7 indicates that the adsorption capacity decreases as temperature increases. These equilibrium data were fitted by the Langmuir model, and the values of K_L were used to determine the thermodynamic parameters according to the following equations:

ΔG° = −RT ln(K_L) (15)

TΔS° = ΔH° − ΔG° (17)

where ΔG° (kJ mol⁻¹), ΔH° (kJ mol⁻¹) and ΔS° (kJ mol⁻¹ K⁻¹) are the changes in free energy, enthalpy and entropy, respectively. ΔH° and ΔS° can be obtained from the slope and intercept of plotting ln(K_L) vs. 1/T (Fig. S3†). These thermodynamic parameters are presented in Table S3.† The negative values of ΔG°, ΔH° and ΔS° confirm that the lead removal IAC has a spontaneous, exothermic nature.

Mechanism of lead adsorption

There are two probable mechanisms for Pb(II) adsorption by IAC nanocomposite, both involving the abundant hydroxyl groups present on the surface of particles, either as hydroxyl groups of AC, or as hydroxyl groups of FeOOH structure. Indeed, such groups could either share their electron pairs to bind with Pb(II) ions and form complexes, or could lose one proton in the context of a ion exchange process involving Pb(II) ions.^61,62 Fig. 8 illustrates the two possible mechanisms for lead removal by IAC nanocomposite.

Fig. 8 Schematic of adsorption mechanism of Pb(II) by IAC nanocomposite.

Conclusions

Commercial activated carbon (AC) was modified via anchorage of iron nanoparticles. Iron-activated carbon nanocomposite (IAC) was synthesized and used as adsorbent for Pb(II) removal from aqueous solution. Characterization tests proved that iron nanoparticles coated on activated carbon surface effectively. The ANOVA tables indicate that solution pH and solid to liquid ratio are the most significant removal parameters and that optimum operating conditions were pH = 6.5, solid to liquid ratio of 3 g L⁻¹ and initial Pb(II) concentration = 10 mg L⁻¹. Under optimum operation condition 96.5% of Pb(II) is removed from dilute solution by IAC. The maximum adsorption capacity of IAC was 121.9 mg g⁻¹. Freundlich model was the best model to describe equilibrium adsorption of lead on IAC. Results showed that chemical adsorption is important in Pb(II) removal.

Acknowledgements

The authors wish to acknowledge the Ministry for Foreign Affairs and International Cooperation of Italy and Amirkabir University of Technology of I.R.Iran for financial support.

Notes and references

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Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c5ra27923b

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