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DOI: 10.1039/C5RA11742A (Paper) RSC Adv., 2015, 5, 79119-79128

Synthesis of regenerable Zn(OH)2 nanoparticle-loaded activated carbon for the ultrasound-assisted removal of malachite green: optimization, isotherm and kinetics

A. A. Bazrafshana, S. Hajati*b and M. Ghaedi*a
aChemistry Department, Yasouj University, Yasouj 75918-74831, Iran. E-mail: m_ghaedi@mail.yu.ac.ir; m_ghaedi@yahoo.com; Fax: +98 74 33223048; Tel: +98 74 33223048
bDepartment of Physics, Yasouj University, Yasouj 75918-74831, Iran. E-mail: hajati@mail.yu.ac.ir; Fax: +98 74 33223048; Tel: +98 74 33223048

Received 18th July 2015 , Accepted 1st September 2015

First published on 1st September 2015

Abstract

Zn(OH)2 nanoparticles (Zn(OH)2-NPs) were sonochemically synthesized. A small amount of Zn(OH)2-NPs was loaded onto activated carbon with a weight ratio of 1[thin space (1/6-em)]:[thin space (1/6-em)]10 followed by characterization using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and diffuse reflectance spectroscopy (DRS). Both activated carbon (AC) and Zn(OH)2 nanoparticle-loaded activated carbon (Zn(OH)2-NP-AC) as safe, green and cost-effective adsorbents were used for the removal of malachite green (MG). Response surface methodology as a cost-effective and time-saving approach was applied to model and optimize dye removal versus adsorbent mass, pH, initial dye concentration and sonication time as well as to investigate the possible interaction among these variables. Zn(OH)2-NP-AC, even at a small nanoparticle loading (with a weight ratio of 1[thin space (1/6-em)]:[thin space (1/6-em)]10), was found to be more efficient than AC. For the Zn(OH)2-NP-AC, the optimum values were found to be 0.019 g, 4.5, 20 mg L−1 and 8.6 min for the adsorbent mass, pH, initial dye concentration and sonication time, respectively. The experimental equilibrium data were then fitted to the conventional isotherm models such as Langmuir, Freundlich, Temkin and Dubinin–Radushkevich. The Langmuir isotherm was found to be the best model for the explanation of the experimental data. The adsorption monolayer capacity of Zn(OH)2-NP-AC was obtained to be 74.63 mg g−1, which is comparable to published reports. Adsorption kinetics was studied at various initial MG concentrations, which showed that the adsorption of MG follows the pseudo-second-order rate equation, in addition to the intraparticle diffusion model. The adsorbent was shown to be highly regenerable over several iterations. The short-time adsorption process, high adsorption capacity and good regenerability of the safe, green and cost-effective Zn(OH)2-NP-AC make it advantageous and promising for wastewater treatment.

1. Introduction

Large amounts of dyes and pigments in the forms of dangerous contaminants are released from industrial sites into the environment. They have mutagenic and carcinogenic effects, which cause numerous human health disorders such as dysfunctions of the kidney, reproductive system, liver, brain, and central nervous system even at low concentrations.1 Malachite green (MG) as a basic dye (Fig. 1), which has numerous industrial applications including the dyeing of silk, leather, plastics, and paper, may harm humans and animals via inhalation and ingestion.2 It may also produce toxicity to the respiratory system and reduce fertility in humans.3 Therefore, it is necessary to remove MG from wastewater.
image file: c5ra11742a-f1.tif
Fig. 1 Chemical structure of malachite green.

Biosorption,4 oxidation,5 photocatalytic degradation6 and adsorption7–11 have been commonly used for such purposes.12,13 Adsorption achieved through the use of highly efficient, cost-effective, non-toxic and easily available adsorbents is the most popular approach.14 Simple-to-design activated carbon-based materials with and without loading satisfy these criteria with favorable capability for the adsorption of high amounts of wide-ranging pollutants.15,16 Adsorption processes using activated carbon (AC) with improved surface areas have been widely proposed and used for the removal of both organic and inorganic pollutants from aqueous effluents.17,18 These adsorbents (particularly on nanoscales) possess high capacities for wastewater treatment.19,20 Ghaedi et al.7–11,18,19 widely studied dye adsorption using nanomaterial-loaded activated carbon. They have shown that the nanomaterial-loaded AC is more efficient than the AC for dye removal because of the improved surface area and number of surface reactive atoms. Metallic nanostructures, such as NiSe and ZnSe nanoparticles,9 Pt nanoparticles,10 ZnS:Cu nanoparticles18 and Cd(OH)2 nanowires,19 have functional groups on their surface and thus are easily loaded onto OH and COOH present at the surface of AC, which causes high improvement in the surface area and adsorbate–adsorbent interaction. On the other hand, ultrasound irradiation significantly enhances the mass transfer from adsorbate to adsorbent in addition to their mixing due to the ultrasound-assisted formation of cavitations. This is in turn because of rapid and local changes in the pressure. The bubbles would undergo violent compression when subjected to high pressure, which can generate an intense shockwave.

In this study, Zn(OH)2 nanoparticles (Zn(OH)2-NPs) were sonochemically synthesized. A small amount of Zn(OH)2-NPs was loaded onto AC with a weight ratio of 1[thin space (1/6-em)]:[thin space (1/6-em)]10. SEM, XRD, FTIR and diffuse reflectance spectroscopy (DRS) were used for their characterization. AC and Zn(OH)2 nanoparticle-loaded AC (Zn(OH)2-NP-AC) were used as safe, green and cost-effective adsorbents for MG removal. Response surface methodology (RSM) as a cost-effective and time-saving method was applied to model and optimize MG removal, whereas adsorbent mass, pH, initial MG concentration and sonication time were considered as variables. RSM was also applied to investigate the possible interaction between the variables involved. The optimal conditions were predicted, the isotherm and kinetics of the adsorption were also studied, and the regenerability of (Zn(OH)2-NPs) was investigated.

2. Experimental procedure

2.1 Instruments and reagents

All chemicals (with analytical reagent grade), including zinc acetate dehydrate (99.9%), NaOH, HCl, AC and MG with the chemical formula of C23H25N2Cl (Mw = 364.911) and a λmax of 617 nm, were purchased from Merck (Darmstadt, Germany). A multiwave ultrasonic generator (UP 200S, Hielscher, Germany) with a titanium horn of 7 mm in diameter operating at 20 kHz with a maximum power output of 200 W was used for ultrasonic irradiation during the synthesis of Zn(OH)2 nanoparticles. X-ray powder diffraction (XRD) spectrum was taken using an X'pert diffractometer of Philips Company (Netherlands) with monochromatized Cu Kα radiation. The pH measurements were carried out using a pH/ion meter model-686 (Metrohm, Switzerland, Swiss). A 130 W and 40 kHz ultrasonic bath with heating system (Tecno-GAZ SPA Ultrasonic System, Bologna, Italy) was used for the ultrasound-assisted adsorption. MG absorbance spectra were acquired using a UV-Vis spectrophotometer (model V-530, Jasco, Japan). The adsorbent morphology was characterized using scanning electron microscopy (SEM, KYKY-EM3200, China). FTIR spectra were obtained using a JASCO-FTIR680 (Japan) instrument over the range 4000–400 cm−1. An Avantes instrument (Avaspec-2048-TEC, Anglia Instruments Ltd, UK) was used for taking diffuse reflectance spectrum from Zn(OH)2-NP powder for its band-gap measurement.

2.2 Methods

The MG stock solution (200 mg L−1) was prepared by dissolving 100 mg of MG in 500 mL distilled water. All working solutions with desired concentrations were prepared daily by diluting the stock solution with double-distilled water. The adsorption experiments were performed in a batch mode, whereas the solution was ultrasonicated at conditions designed under RSM. The adsorbent (either AC or Zn(OH)2-NP-AC) was separated by centrifugation for 15 min. The dilute phase was analyzed for the determination of MG concentration using UV-Vis spectrophotometry at wavelength of 617 nm. The MG removal percentage was calculated from a calibration graph.

2.3 Synthesis of Zn(OH)2-NPs

To synthesize zinc hydroxide nanoparticles (Zn(OH)2-NPs), 50 mL aqueous solution of NaOH (0.2 M) was added to the aqueous solution of Zn(CH3COO)2·2H2O (0.1 M) at a rate of 1 mL min−1 under ultrasonic irradiation using a probe directly immersed into the solution.

2.4 Preparation of Zn(OH)2-NP-AC

Activated carbon was powdered and sieved with a sieve mesh of 100 μm. The zinc hydroxide nanoparticles (Zn(OH)2-NPs) were loaded onto the AC with a weight ratio 1[thin space (1/6-em)]:[thin space (1/6-em)]10 in the following manner: 2.0 g AC was thoroughly dispersed for 20 min in 150 mL of water under sonication. 0.2 g Zn(OH)2-NP was dispersed for 20 min in 50 mL of water. Both the solutions were then intermixed and sonicated for 15 min and then stirred for 15 h at 300 rpm. Zn(OH)2-NP-AC was separated by centrifugation and dried for 18 h at 80 °C.

2.5 Central composite design under response surface methodology

Central composite design (CCD) under RSM was applied to design a systematic series of experiments (31 runs) in five levels. RSM makes it possible to nonlinearly model the experimental data.21–23 CCD avoids running unnecessary experiments while helping to investigate the synergies among the variables. In other words, CCD under RSM helps to analyze the interaction between the parameters. The initial MG concentration (X1), adsorbent dosage (X2), pH (X3) and sonication time (X4) were involved in the CCD as variables, which may affect the MG (R%MG) removal percentage as a response. The experimental sequence was randomized to minimize the effects of uncontrolled factors (Table 1). A detailed explanation on CCD under RSM has been reported elsewhere.24–29
Table 1 Experimental factors, levels and matrix of CCD
Factors Level Low (−1) Central (0) High (+) Star point α = 2.0
−α +α
(X1) MG concentration (mg L−1) 10 15 20 5 25
(X2) adsorbent dosage (g) 0.0060 0.0105 0.0150 0.0015 0.0195
(X3) pH 3.5 5 6.5 2 8
(X4) sonication time (min) 3.0 5.5 8.0 0.5 10.5
Run (X1) (X2) (X3) (X4) R%MG
1 15 0.0105 5.0 5.5 94.48
2 20 0.0060 6.5 8.0 70.03
3 10 0.0060 3.5 8.0 69.30
4 20 0.0060 6.5 3.0 52.02
5 15 0.0105 5.0 0.5 79.38
6 10 0.0060 3.5 3.0 65.40
7 25 0.0105 5.0 5.5 70.70
8 20 0.0060 3.5 8.0 65.01
9 10 0.0150 3.5 8.0 96.58
10 15 0.0105 5.0 5.5 89.80
11 20 0.0150 3.5 3.0 77.81
12 15 0.0105 8.0 5.5 84.16
13 10 0.0150 6.5 8.0 97.85
14 15 0.0105 2.0 5.5 77.51
15 10 0.0060 6.5 8.0 83.95
16 15 0.0015 5.0 5.5 53.03
17 15 0.0105 5.0 10.5 98.48
18 10 0.0150 6.5 3.0 95.10
19 20 0.0150 3.5 8.0 93.20
20 20 0.0060 3.5 3.0 51.02
21 15 0.0105 5.0 5.5 87.85
22 15 0.0105 5.0 5.5 88.46
23 20 0.0150 6.5 8.0 94.30
24 15 0.0195 5.0 5.5 99.50
25 10 0.0060 6.5 3.0 75.90
26 10 0.0150 3.5 3.0 96.77
27 20 0.0150 6.5 3.0 73.50
28 15 0.0105 5.0 5.5 92.10
29 5 0.0105 5.0 5.5 99.87
30 15 0.0105 5.0 5.5 85.90
31 15 0.0105 5.0 5.5 84.40

The response may be modeled as:

 
image file: c5ra11742a-t1.tif(1)
where Y is the predicted response (removal percentage) and Xi's are independent variables (MG concentration, adsorbent dosage, pH and sonication time) that are known for each experimental run. The parameter β0 is the model constant, βi's are the linear coefficients, βii's are the quadratic coefficients, and βij's are the interaction-term coefficients. The main interaction and quadratic effects were evaluated in this design. The analysis of variance (ANOVA) was performed to determine the significance level of each term (Table 2). From the significant terms, the model (response function) was reconstructed and validated to determine the best operating conditions of the process. The significance of each variable was checked from the corresponding P- and F-values. A P-value of less than 0.05 in the ANOVA table indicates the statistical significance of a variable at a 95% confidence level.

Table 2 Analysis of variance for full quadratic model
Source DF Adj SS Adj MS F-value P-value
Model 14 6098.04 435.57 34.55 0.000
Linear 4 5170.51 1292.63 102.54 0.000
X1 1 1097.55 1097.55 87.06 0.000
X2 1 3394.36 3394.36 269.25 0.000
X3 1 69.56 69.56 5.52 0.032
X4 1 609.03 609.03 48.31 0.000
Square 4 617.96 154.49 12.25 0.000
X12 1 76.14 76.14 6.04 0.026
X22 1 431.99 431.99 34.27 0.000
X32 1 215.35 215.35 17.08 0.001
X42 1 14.84 14.84 1.18 0.294
Interaction 6 309.57 51.60 4.09 0.011
X1X2 1 5.04 5.04 0.40 0.536
X1X3 1 30.09 30.09 2.39 0.142
X1X4 1 180.10 180.10 14.29 0.002
X2X3 1 75.60 75.60 6.00 0.026
X2X4 1 1.69 1.69 0.13 0.719
X3X4 1 17.06 17.06 1.35 0.262
Error 16 201.71 12.61    
Lack-of-fit 10 129.04 12.90 1.07 0.491
Pure error 6 72.66 12.11    
Total 30 6299.75      

The desirability function (DF) was applied to investigate the optimal conditions based on Derringer's desirability function.30,31

3. Results and discussion

3.1 Characterization

Fig. 2a shows the XRD pattern obtained from Zn(OH)2-NPs, which is consistent with the standard JCPDS card 31-0228 corresponding to the wurtzite-structured Zn(OH)2-NPs.
image file: c5ra11742a-f2.tif
Fig. 2 (a) XRD pattern of Zn(OH)2 prepared by the sonochemical process; (b) SEM image of Zn(OH)2; (c) FTIR spectra of AC, Zn(OH)2-NP and Zn(OH)2-NP-AC; and (d) DRS taken from Zn(OH)2-NP and the plot of (αhν)2 versus hν (inset).

The morphology and size of Zn(OH)2-NPs were studied by scanning electron microscopy (Fig. 2b). It can be observed that the average size of nanoparticles is about 60 nm.

The characteristic functional groups of Zn(OH)2-NPs, Zn(OH)2-NP-AC and AC were investigated using the FTIR spectra (Fig. 2c). A peak appearing around 500 cm−1 corresponds to the stretching mode of Zn–O bonds in the spectra of Zn(OH)2-NPs and Zn(OH)2-NP-AC. The broad band at around 3426 cm−1 was assigned to the symmetric and asymmetric O–H stretching vibrations of water present in Zn(OH)2-NPs and/or AC functional groups. The peak appearing at 1500 cm−1 is due to H–O–H bending. In other words, the observed peaks in the range of 1500–3500 cm−1 are probably attributed to either the absorbed water molecules in the KBr matrix, the prepared nanostructures, or their probable interaction. The observed peak around 1100 cm−1 is due to the single C–C bond stretching mode of acetate solution used.

Diffuse reflectance spectrum (DRS) of Zn(OH)2-NPs known as a direct band-gap material was taken over the energy range from 2 eV to 4 eV (Fig. 2d). The energy band gap was determined using a Tauc plot, which is the plot of (αhν)2 versus hν (inset of Fig. 2d). The band-gap value at 25 °C was determined to be 3.34 eV by extrapolating the straight-line portion of (αhν)2 to cross the hν axis, as shown in the inset of Fig. 2d. This value is higher than that of bulk Zn(OH)2 which is 3.2 eV, indicating nanoparticle formation based on the fact that the band gap of each material becomes larger as its dimension decreases.

3.2 Modeling and process optimization

The ANOVA for the full quadratic model (Table 2) shows a non-significant lack of fit (0.491), which means the applicability of this model. However, according to this ANOVA, the terms X1, X2, X3, X4, X12, X22, X32, X1X4 and X2X3 are significant for MG removal, indicating the linear influence of the MG concentration, adsorbent dosage, pH and sonication time on the MG removal. The response was also shown to be affected quadratically by the first three factors. The MG concentration–time as well as the adsorbent dosage–pH interaction terms were found to be considerably effective. Therefore, the full quadratic model should be reduced to the following model:
 
R%MG = −3.7 − 0.22X1 + 8068X2 + 17.94X3 − 1.82X4 − 0.065X12 − 191937X22 − 1.22X32 + 0.268X1X4 − 322X2X3 (2)

The coefficients of determination R2 and adjusted R2 were found to be 0.967 and 0.940, respectively. Fig. 3 shows good agreement between the experimental and calculated removal percentage values.


image file: c5ra11742a-f3.tif
Fig. 3 Experimental versus predicted R%MG.

The profile for predicted values and desirability option (not shown) was used to optimize the adsorption process. Profiling the desirability of response involves specifying the desirability function (DF) for R%MG as a dependent variable by assigning the predicted R%MG values. The scale in the range of 0.0 (undesirable) to 1.0 (very desirable) is used to obtain a comprehensive function that should be maximized according to the efficient selection and optimization of designed variables. The CCD design matrix results (Table 1) show the maximum (99.87%) and minimum (51.02%) of R%MG. Regarding this range, the optimum condition, at which the maximum response is achieved, was obtained. From these calculations and desirability score of 1.0, a maximum recovery of 99.33% was obtained at optimum conditions. The optimal values for the factors MG concentration, adsorbent dosage, pH and sonication time were found to be 20 mg L−1, 0.019 g, 4.5 and 8.6 min, respectively. At this condition, R%MG was predicted to be 99.32% with desirability of 0.99. The validity of the predicted response at optimum conditions was checked by performing three experiments under similar conditions. On average, the experimental response was obtained to be 99.15%, which is in excellent agreement with the predicted value.

MG dye adsorption onto AC and Zn(OH)2-NP-AC was studied at optimum values of variables involved. The results are presented in Table 3. The higher dye removal percentage for Zn(OH)2-NP-AC, even at a very low amount of nanoparticle loading (with a weight ratio of 1[thin space (1/6-em)]:[thin space (1/6-em)]10), may be attributed to its surface area and the number of reactive atoms.7–11

Table 3 Comparison of MG removal from an aqueous solution by AC and Zn(OH)2-NP-AC at optimum conditions
Adsorbent AC Zn(OH)2-NP-AC
R%MG 93.35 99.06
89.75 99.20
91.1 99.18
Average R%MG 91.40 99.15

The contact time necessary to reach equilibrium depends on the initial dye concentration, pH and amount of adsorbent.32 The amount of adsorbed MG at equilibrium (qe (mg g−1)) was calculated as follows:

 
qe = (C0 − Ce)V/W (3)
where C0 and Ce (mg L−1) are the liquid-phase concentrations of the dye at initial and equilibrium, respectively. V (L) is the volume of the solution, and W (g) is the mass of dry adsorbent used.

3D response surfaces of significant interactions are shown in Fig. 4. The variation of MG removal versus sonication time and initial MG concentration is shown in Fig. 4a, where other variables maintained their optimal values. It can be observed for lower concentrations of MG that shorter time is required for adsorption because of the compromised number of adsorbent vacant sites and dye molecules. For further MG removal, more elapsed time is needed. At higher MG concentrations, the change in adsorption time is due to the decrease in diffusion rate and concentration gradient. Fig. 4b shows the extent to which R%MG is affected by the adsorbent mass and pH. It can be observed that R%MG increases by increasing the adsorbent amount, whereas its variation versus pH does not monotonically increase; thus, the apex of negative parabola occurs at pH 4.5 as an optimum value. By varying the pH, the charges of the adsorbent surface and dye molecules are varied and thus the mechanism and strength of the dye adsorption may be affected. At a lower pH, the proton (H+) is abundant in aqueous media and thus the charge of adsorbent surface and MG molecules becomes more positive. This phenomenon causes an electrostatic repulsive force between the adsorbent and adsorbate, which reduces R%MG. At pH far greater than 4.5, the charge of both adsorbent and adsorbate becomes negative, and thus the electrostatic repulsive force plays a role to reduce the dye adsorption.


image file: c5ra11742a-f4.tif
Fig. 4 Response surface for the variation of R%MG against (a) MG concentration and contact time and (b) adsorbent mass and pH.

Moreover, in the proposed process, the MG dye molecules initially must encounter a boundary layer effect and subsequently diffuse into the porous structure of the adsorbent over a longer contact time. The MG molecule can diffuse to most adsorbent pores, which takes a relatively long time. An increase in the adsorption with increasing adsorbent dosage can be attributed to increased adsorbent surface area and the availability of more adsorption sites.33

3.3 Adsorption isotherms

Adsorption equilibrium isotherms mathematically relate the amount of adsorbed target per gram of adsorbent (qe (mg g−1)) to the equilibrium solution concentration (Ce (mg L−1)) at a fixed temperature. This investigation is of high importance in both theoretical and practical points of view to obtain extensive knowledge about the surface properties of adsorbents and the removal mechanism.34

Various isotherms such as Freundlich, Langmuir, Dubinin–Radushkevich (D–R) and Temkin models may be applied to fit the equilibrium data of the adsorption system. These isotherms are generally used to fit the experimental equilibrium data to investigate the efficiency and cost-effectiveness of the procedure for the removal of high amounts of pollutants using a small amount of adsorbent.

The Langmuir model35–37 describes a monolayer sorption of a target compound on a homogenous surface, whereas the Freundlich model explains a multi-layer adsorption process.38,39 The correlation coefficient corresponding to each isotherm was obtained as a good criterion for the judgment on the applicability of that isotherm to describe the equilibrium data. From the linear plots of the isotherms, all the respective constants, along with their correlation coefficients, were calculated at several adsorbent masses. The full description of adsorption isotherms and the values of corresponding parameters are presented in Table 4.

Table 4 Isotherm constants of MG adsorption onto Zn(OH)2-NP-AC
Isotherm Equation   Parameters Adsorbent (g)
0.014 0.019 0.024
Langmuir Ce/qe = Ce/Qm + 1/KaQm The slope and intercept of linear plot of Ce/qe versus Ce provide Qm and Ka, respectively Qm (mg g−1) 71.429 74.627 62.5
ka (L mg−1) 2 19.706 266.667
RL 0.032–0.020 0.0034–0.002 0.00025–0.00015
R2 0.994 0.9997 1
Freundlich ln[thin space (1/6-em)]qe = (1/n)ln[thin space (1/6-em)]Ce + ln[thin space (1/6-em)]KF The slope and intercept of linear plot of ln[thin space (1/6-em)]qe versus ln[thin space (1/6-em)]Ce provide 1/n and KF, respectively 1/n 0.103 0.213 0.095
KF (L mg−1) 49.545 70.637 59.156
R2 0.797 0.9 0.624
Temkin qe = Bl[thin space (1/6-em)]ln[thin space (1/6-em)]Ce + Bl[thin space (1/6-em)]ln[thin space (1/6-em)]KT B1 and KT are calculated from the slope and intercept of linear plot of qe against ln[thin space (1/6-em)]Ce, respectively Bl 5.106 10.01 3.784
KT (L mg−1) 23[thin space (1/6-em)]086.505 1012.401 4[thin space (1/6-em)]820[thin space (1/6-em)]931.9
R2 0.684 0.941 0.729
Dubinin and Radushkevich ln[thin space (1/6-em)]qe = −Kε2 + ln[thin space (1/6-em)]Qs, (ε = RT[thin space (1/6-em)]ln (1 + 1/Ce)) The slope and intercept of the linear plot of ln[thin space (1/6-em)]qe versus ε2 provide K and Qs, respectively Qs (mg g−1) 58.207 70.316 56.148
K(10−9) 6.00 9.00 2.00
R2 0.626 0.976 0.611

In the Freundlich isotherm, the constant of Kf provides information on the bonding energy and is known as the adsorption or distribution coefficient, representing the quantity of dye adsorbed onto the adsorbent. 1/n shows the strength of dye adsorption onto the adsorbent (i.e. surface heterogeneity). By increasing the heterogeneous nature of its surface, its value gets closer to zero. The 1/n value of less than 1 indicates normal Langmuir and cooperative adsorption. The value of 0.213 at optimum condition confirms that the adsorption isotherm follows normal Langmuir.

It can be observed in Table 4 that the correlation coefficient (R2) of the Freundlich model (0.8, 0.9 (optimal condition) and 0.62) was obtained to be lower than that of the Langmuir model (0.99, 1 (optimized condition) and 1), which suggests that the removal process well follows the monolayer adsorption. In addition, correlation coefficients (R2) of the Temkin40 and D–R (applied to estimate the porosity, free energy and the characteristics of the adsorbents)41 models are lower than that of the Langmuir. The high correlation coefficient (R2 > 0.99) with maximum monolayer capacity (74.63 mg g−1) from the Langmuir model proves strong applicability of the Langmuir model to explain the equilibrium data for the MG adsorption onto Zn(OH)2-NP-AC (Table 4).

3.4 Kinetics of the MG adsorption onto Zn(OH)2-NP-AC

Every adsorption process may follow several different patterns such as chemical reaction, diffusion control and mass transfer. The analysis of experimental data at different times makes it possible to calculate the kinetic parameters to obtain some information and thus to model and design the adsorption processes, the nature of which depends on the physical or chemical characteristics of the adsorption system.

The experimental kinetic data, obtained at various initial MG concentrations (15, 20 and 25 mg L−1) and optimal adsorbent mass (0.019 g), were fitted to different adsorption kinetic models such as pseudo-first-order,42 pseudo-second-order,43 Elovich44 and intraparticle diffusion45 to determine the rate and mechanism of the adsorption process (Table 5). The definition of the parameters presented in Table 5 are presented in the following manner: in the pseudo-first-order42 and pseudo-second-order models, qe (mg g−1) is the amount of MG adsorbed at equilibrium and qt (mg g−1) is the amount of MG adsorbed at time t. k1 (min−1) and k2 (g mg−1 min−1) are the rate constants of pseudo-first-order and pseudo-second-order adsorption, respectively.

Table 5 Kinetic parameters of MG adsorption onto Zn(OH)2-NP-AC
Model Equation     Calculated parameters
15 20 25
Dye concentration (mg L−1)
First-order kinetic ln(qe − qt) = −k1t + ln(qe) The slope and intercept of linear plot of ln(qe − qt) versus t provide k1 and qe, respectively k1 2.024 2.213 2.332
qe (calc) 205.873 2026.749 5473.940
R2 0.603 0.530 0.650
Second-order kinetic t/qt = t/qe + 1/(k2qe)2 The slope and intercept of linear plot of t/qt versus t provide qe and k2, respectively k2 0.4885 0.080 0.027
qe (calc) 39.68 53.19 64.94
R2 1 0.999 0.998
Intraparticle diffusion qt = Kdifft1/2 + C The slope and intercept of linear plot of qt versus t1/2 provide Kdiff and C, respectively Kdiff 0.710 2.826 8.287
C 37.49 43.82 38.25
R2 0.868 0.972 0.952
Elovich qt = 1/β[thin space (1/6-em)]ln(t) + 1/β[thin space (1/6-em)]ln(αβ) β and α are obtained from the slope and intercept of the plot of qt versus ln(t), respectively β 1.500 0.398 0.131
R2 0.925 0.930 0.966
α 4.14 × 1024 3.48 × 1030 1.97 × 1030
qe(exp) 39.367 52.130 60.926

The kinetic model for the adsorption of a solute by a solid in an aqueous solution is generally complex. The adsorption rate is strongly influenced by several parameters related to the state of the solid (generally with a highly heterogeneously reactive surface) and to the physicochemical conditions under which the adsorption occurs. The consistency between the experimental and the model-predicted data was investigated by calculating correlation coefficients (R2) and by observing the extent to which the experimental adsorption capacity is close to the theoretical value. The equilibrium adsorption capacity, qe, was observed to increase from 39.68 mg g−1 to 64.98 mg g−1 by increasing the MG concentration from 15 mg L−1 to 25 mg L−1 at 0.0195 g of Zn(OH)2-NP-AC.

High inconsistency between the experimental data and the pseudo-first-order model was observed (Table 5). In pseudo-second-order model, the calculated qe values are very close to that of the experimental data and R2 values were found to be very close to 1.0, indicating that the MG adsorption onto Zn(OH)2-NP-AC obeys the pseudo-second-order kinetic model for the entire sorption period. The rate-constant values obtained from the pseudo-second-order kinetic model were found to decrease from 0.4885 to 0.027 g mg−1 min−1 by increasing the initial MG concentration from 15 to 25 mg L−1 at 0.0195 g of adsorbent.

The linear relation of initial dye concentration with removal rate fails when pore diffusion is the predominant stage and limits the adsorption process. The dye transfer from the bulk to the solid phase may occur through an intraparticle diffusion/transport process known as the rate-limiting step.46 In the intraparticle diffusion model, the value of intercept C (Table 5) provides information about the thickness of the boundary layer and the external mass transfer resistance. The constant C was found to be 37.49, 43.82 and 38.25 at MG concentrations of 15, 20 and 25 mg L−1, respectively, whereas the Zn(OH)2-NP-AC mass was set to be 0.0195 g. This variation is attributed to the increase in the boundary layer thickness, which decreases the chance of external mass transfer and subsequently increases the amount of internal mass transfer. In addition to the applicability of the pseudo-second-order kinetic model, the intraparticle diffusion model was also found to be applicable to explain the experimental data (Table 5) with high R2 value (0.97 at an optimal adsorbent mass). This may confirm that the rate-limiting step is the intraparticle diffusion process. The intraparticle diffusion rate-constant (Kdiff) values were obtained to be 0.710, 2.826 and 8.287 mg g−1 min−1/2 at 0.019 g of adsorbent with good positive correlation to the initial dye concentration. This linear relationship shows a high contribution of intraparticle diffusion on the adsorption process.

3.5 Comparison with other adsorbents for the MG removal

Several adsorption processes have been reported for MG adsorption.26,47–55 It can be observed in Table 6 that our process is one of the best adsorption systems and is advantageous in terms of the contact time (8.6 min) and adsorption capacity (74.63 mg g−1).
Table 6 Comparison of the present method with some previously reported MG adsorption systems
Adsorbent Adsorption capacity (mg g−1) Contact time (min) References
AC 4.34 89 26
Activated carbon 75.08 1440 47
Activated slag 74.16 1440 47
Cyclodextrin-based material 91.9 120 48
Bentonite clay 7.72 10 49
Commercial activated carbon 8.27 15 50
Laboratory grade activated carbon 42.18 15 50
Arundo donax-root carbon 8.69 180 51
ZnO-NP-AC 76.92 15 52
ZnO-nanorod-AC 59.17 4 53
CoFe2O4-AC 20 89.3 54
Pisum sativum 35 6.17 55
Zn(OH)2-NP-AC 74.63 8.6 This work

3.6 Determination of point of zero-charge pH (pHpzc) of adsorbent

The point of zero-charge pH (pHpzc) measurement was performed for both AC and Zn(OH)2-NP-AC by the final pH drift method, which was previously reported elsewhere.11 A 10 mL solution of KNO3 (0.1 M) was added to 0.1 g of AC. The initial pH values were adjusted in the range 1–8 by adding HCl or NaOH solutions, and the solution was then stirred for 24 h at room temperature. After filtering the mixture, the difference between the final pH (pHf) and initial pH (pHi) of the solution was plotted against pHi (Fig. 5). The adsorbent is neutral when the solution pH is equal to pHpzc. The adsorbent surface is negatively charged at pH values greater than pHpzc and positively charged at pH values lower than pHpzc.11,56 The pHpzc values for AC and Zn(OH)2-NP-AC were determined to be 1. Therefore, the surfaces of both AC and Zn(OH)2-NP-AC are positively charged in solutions at a pH lower than 1, whereas they are negatively charged at a pH above 1. The maximum difference between pHf and pHi obtained at an initial pH of about 4.5 confirms the optimal value predicted by RSM and verified by the experiments.
image file: c5ra11742a-f5.tif
Fig. 5 pHf–pHi versus pHi to determine pHpzc.

3.7 Regeneration of adsorbent

Based on the optimal condition, a 10 mL solution of 20 mg L−1 MG and 3.8 mg of Zn(OH)2-NP-AC were mixed and sonicated for 8.6 min. Subsequently, the mixture was centrifuged for 15 min and the MG removal percentage was determined to be 99.8%. The regeneration of adsorbent was then investigated using various solvents such as ethanol, methanol, acetone, acetonitrile, N,N-dimethelformamide, benzene and water. High efficiency was found for methanol for the well regeneration of Zn(OH)2-NP-AC. MG dye adsorbed by the adsorbent was extracted using 10 mL methanol at pH 10.5 by a three-stage washing procedure. The pH of the extracted solution was then adjusted to 4.5 for reading the MG adsorption. The MG extraction percentages from the adsorbent were found to be 87%, 90%, 92%, 91% and 75% for the first to fifth regenerations, respectively, showing very good regeneration of the adsorbent.

4. Conclusion

The sonochemically synthesized Zn(OH)2 nanoparticles were loaded on AC and used as an efficient and regenerable adsorbent. The adsorbent was successfully used for the ultrasound-assisted removal of the MG dye within a short time (8.6 min). Response surface methodology was successfully applied, and it was found that the initial MG concentration, adsorbent mass, pH and sonication time linearly affect R%MG. The response was also shown to be affected quadratically by the first three factors. The interaction between MG concentration and time, as well as the interaction between the adsorbent mass and pH, were found to be significant, which considerably affect R%MG. Zn(OH)2-NP-AC was found to be an efficient and well regenerable adsorbent with high adsorption capacity (74.63 mg g−1), which is comparable to other adsorbents studied. The Langmuir isotherm was found to be best applicable model describing the experimental equilibrium data. The adsorption kinetics data were well fitted to the pseudo-second-order kinetics model. Furthermore, it was shown that the intraparticle diffusion model also applies.

Acknowledgements

The authors thank the Yasouj University and the Iran National Science Foundation (INSF, contract: 93023641) for supporting this work.

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