Adsorption of ethidium bromide (EtBr) from aqueous solutions by natural pumice and aluminium-coated pumice

Journal of Molecular Liquids 213 (2016) 41–47

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Adsorption of ethidium bromide (EtBr) from aqueous solutions by natural pumice and aluminium-coated pumice Behzad Heibati a, Kaan Yetilmezsoy b, Mohammad Ali Zazouli c, Susana Rodriguez-Couto d,e,⁎, Inderjeet Tyagi f, Shilpi Agarwal f,g, Vinod Kumar Gupta f,g,⁎⁎ a

Health Sciences Research Centre, Faculty of Health, Student Research Committee, Mazandaran University of Medical Sciences, Sari, Iran Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Esenler, Istanbul, Turkey Faculty of Health and Health Sciences Research Center, Department of Environmental Health Engineering, Mazandaran University of Medical Sciences, Sari, Iran d CEIT, Unit of Environmental Engineering, Paseo Manuel de Lardizábal 15, 20018 San Sebastian, Spain e IKERBASQUE, Basque Foundation for Science, María Díaz de Haro 3, 48013 Bilbao, Spain f Department of Chemistry, Indian Institute of Technology Roorkee, 247667, India g Department of Applied Chemistry, University of Johannesburg, Johannesburg, South Africa b c

a r t i c l e

i n f o

Article history: Received 12 August 2015 Accepted 26 August 2015 Available online xxxx Keywords: Artificial neural network Ethidum bromide Isotherms Kinetics Pumice stone

a b s t r a c t In the present paper, the removal of ethidium bromide (EtBr) from aqueous solutions in a batch system using natural (NP) and aluminium-coated pumice (ACP) as alternative low-cost adsorbents was investigated. The maximum adsorption capacity, qm (mg/g) was 58.82 and 76.92 mg/g for NP and ACP, respectively, operating at an initial pH of 8, an adsorbent dose of 8 g/L, a contact time of 210 min and an initial EtBr concentration of 30 mg/L. The equilibrium data of both adsorbents fitted the Freundlich isotherm model, indicating the heterogeneity of the adsorbent surface. In addition, the adsorption rate of both adsorbents was well described by the pseudo-second-order kinetics model. This indicated chemisorption was the rate-controlling step of the adsorption process which occurred by ion exchange. Within the performed study, a three-layer artificial neural network (ANN) model was also developed to predict the efficiency of EtBr removal. Computational results clearly demonstrated that the ANN model was able to predict the combined effect of initial pH, adsorbent dose, contact time and initial EtBr concentration on the adsorption efficiency with a very high determination coefficient (R2 = 0.98) and a low relative error (RE = 0.037). © 2015 Elsevier B.V. All rights reserved.

1. Introduction Ethidium bromide (3, 8-diamino-6-phenyl-5-ethylphenanthridinium bromide, 2, 7-Diamino-10-ethyl-9-phenylphenanthridinium bromide; EtBr) is a dark red, crystalline and water soluble compound [35], widely used in research laboratories for the fluorescent detection of DNA [30]. EtBr is highly mutagenic [21] and, thus, proper treatment before its disposal is required. Currently, different methods have been used to remove EtBr from aqueous solutions such as chemical degradation [20], electrochemical degradation [35], photocatalytic degradation [1,2] and incineration [24]. Chemical degradation of EtBr is difficult and requires the use of expensive reagents which increases the price of the treatment

⁎ Correspondence to: S. Rodriguez-Couto, CEIT, Unit of Environmental Engineering, Paseo Manuel de Lardizábal 15, 20018 San Sebastian, Spain. ⁎⁎ Correspondence to: V. K. Gupta, Department of Chemistry, Indian Institute of Technology Roorkee, 247667, India. E-mail addresses: [email protected] (B. Heibati), [email protected] (K. Yetilmezsoy), [email protected] (M.A. Zazouli), [email protected] (S. Rodriguez-Couto), [email protected] (V.K. Gupta).

http://dx.doi.org/10.1016/j.molliq.2015.08.063 0167-7322/© 2015 Elsevier B.V. All rights reserved.

[36–51]. Also, biological methods have shown to be ineffective in removing EtBr because of its resistance to biodegradation [35]. Adsorption is an efficient process for the removal of environmental pollutants from water and wastewater [11] and several researchers have already shown that it can be a useful method to remove EtBr from aqueous solutions [19,20,28]. A wide variety of natural and synthetic materials have been tested as EtBr adsorbents including CuO nanoparticles [7], single-walled carbon nanotubes [28] and polymeric resins [6]. Commercial activated carbon and carbon nanotubes are expensive and need to be regenerated continuously to reduce the process cost [11]. Therefore, the development of low-cost materials for EtBr removal is highly interesting. Thus, the applicability of local low-cost adsorbents is being assessed. In this sense, in recent years pumice stone, in both natural and modified forms, has been used for the removal of fluoride [23], azo dyes [22], phenol and 4-chlorophenol [3], heavy metals [26], SO2 [29] and natural organic matter [16]. To improve the adsorption capacity of the naturally-occurring adsorbents, various metals such as aluminium [13], iron, manganese [8] and copper [17] have been used. Due to the several advantages of pumice stone and its availability in Iran, the objective of this study was to investigate the possible use of

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natural (NP) and aluminium-coated pumice (ACP) as alternative lowcost adsorbents for the removal of EtBr from aqueous solutions. Additionally, the experimental system was modelled by using artificial neural networks (ANNs). An ANN is simply based on the understanding of biological nervous systems where the relation between inputs and outputs is not clearly known. ANN is a parallel system that consists of several processing elements connected by links of variable weights and biases. Among many ANN structures, the multi-layer back-propagation network is by far the most popular [32–34]. ANNs can solve high dimensional complex engineering problems due to their ability to identify a relationship from given patterns [10]. On the basis of batch adsorption experiments, a three-layer ANN model was used to predict the EtBr adsorption efficiency of NP and ACP. Adsorption of EtBr from aqueous solutions was optimised to determine the optimal network structure and the outputs obtained from the models were compared with the experimental data. Also, the kinetics of the adsorption system was studied. 2. Materials and methods 2.1. Reagents All the chemicals used in this study were purchased from Merck Chemical Corp. (Germany) and used as received without further purification. The molecular weight of EtBr (C21H20BrN3) is 394.294 g/mol and has a maximum absorption wavelength (λmax) in the visible spectrum at 480 nm. 2.2. Preparation of NP Pumice stone was obtained from the Tikmeh Dash region, located in the southeast of the Azerbaijan province, northwestern area of Iran. To increase its porosity, the natural pumice (NP) was washed with double-distilled water (DDW) several times and then kept in 1 N HCl for 24 h. After that, the NP was rinsed with DDW several times until

its effluent turbidity reached at least 0.1 NTU. Finally, the pre-treated NP was crushed and sieved to 20 meshes [12]. 2.3. Preparation of ACP To coat the surface of NP with aluminium, the NP was crushed by a jaw crusher and screened by a sieve (Mesh No. 20). Then, 50 g of pumice particles were added into a beaker containing 150 mL of 0.5 M Al2(SO4)3·18H2O. The pH was adjusted to 11 by adding 10 N NaOH dropwise under stirring. Thereafter, the beaker was kept under static conditions at laboratory temperature (25 ± 1 °C) for 72 h. Afterwards, it was dried in an oven (Electro-mag, M 6040 P) at 110 °C for 14 h. In order to remove the traces of uncoated aluminium from the dried pumice particles, they were washed again with DDW and dried in the oven at 105 °C for 14 h [13]. The mineralogical composition of the adsorbents was determined by X-ray diffraction (XRD) and the morphology of the pumice stone before and after modification was characterised by Scanning Electron Microscopy (SEM, Philips-XL30, Holland). Also, the surface area of the adsorbents was determined using the Brunauer, Emmett and Teller (BET) method. 2.4. Adsorption experiments A stock solution of EtBr in deionised water (1000 mg/L) was prepared and the required initial concentrations of EtBr standards were prepared by appropriate dilutions of this stock solution. Batch experiments were conducted in 100-mL Erlenmeyer flasks containing 50 mL of different EtBr standard solutions and different doses of adsorbent depending on the experiment. 1 N HCl or 1 N NaOH were used to adjust the pH. The flasks were maintained in an incubator (25 ± 1 °C) under shaking (120 rpm). After reaching the equilibrium time, the samples were filtered prior to EtBr determination. The amount of residual EtBr concentration was determined at 480 nm, which is the maximum wavelength adsorption of EtBr in the visible spectrum, by means of a UV–VIS spectrophotometer

Fig. 1. SEM images of natural pumice (A) and aluminium-coated pumice (B); X-ray diffraction spectrum (XRD) of natural pumice (C) and aluminium-coated pumice (D).

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Lambda 25 (PerkinElmer, Norwalk, CT, USA). The process parameters studied were contact time (1–210 min), initial EtBr concentration (ranging from 10 to 100 mg/L for isotherm studies and 30 and 100 mg/L to study the effect of initial dye concentration on EtBr adsorption), initial pH (1–10) and adsorbent dose (1–10 g/L). All measurements were performed at a constant temperature of 25 ± 1 °C. 2.5. Modelling approach In this work, an artificial neural network (ANN) tool within the framework of a SPSS™ software was used to predict the adsorption efficiency. A three-layer ANN, an input layer with 5 neurons (initial pH, adsorbent dose, contact time, adsorbent type (NP and ACP) and initial EtBr concentration), a hidden layer with 3 neurons and an output layer with 1 neuron, was established. A hyperbolic tangent function was used at a hidden layer as an activation function. The least square methodology was implemented for the error minimization. The best back-propagation (BP) training algorithm was investigated by using a simulated annealing (SA) algorithm. Although other optimization algorithms (i.e., hill climbing, genetic algorithms, gradient descent, etc.) can be effective at finding a good solution, they also have a tendency to get stuck in local optimums. On the other hand, the SA algorithm is excellent at avoiding the above-mentioned problem and is much better on average at finding an approximate global optimum. Following benchmark comparisons, an optimization was carried out for the best-fit BP algorithm. Then the three-layer ANN was evaluated by the best BP algorithm. 3. Results and discussion 3.1. Adsorbent characteristics The solid structure and morphology of the external surface of the NP and ACP analysed by SEM are shown in Fig. 1A and B. Ordered silica crystals on the surface of NP and ACP and micro-pores or roughness with small cracks on the NP surface can be observed. The solid structure of the adsorbents analysed by Dispersive X-ray diffraction (EDX) is shown in Fig. 1C and D. The EDX analysis of NP indicated the following elemental composition (wt.%): Al — 13.55 ± 0.08, Si – 63.81 ± 0.09, K — 6.02 ± 0.004, Fe — 16.62 ± 0.07. Likewise, the elemental composition of ACP was Al — 17.08 ± 0.04, Si — 71.19 ± 0.07, K — 5.61 ± 0.004, Fe — 6.12 ± 0.04. The surface area determined by the BET method was found to be 8 and 25 m2/g for NP and ACP, respectively. 3.2. Effect of contact time and initial EtBr concentration on EtBr adsorption EtBr adsorption increased with time until reaching a constant value after 210 min for both adsorbents (i.e. NP and ACP), corresponding to adsorbent saturation, operating at an initial pH of 8, an adsorbent dose of 8 g/L and initial EtBr concentrations of 30 and 100 mg/L. It was seen that for both concentrations the adsorption of EtBr increased with time up to about 210 min for both adsorbents and then it remained constant (Fig. 2A). This could be explained by the abundant availability of active sites on the adsorbents at the beginning of the process, whereas with the gradual occupancy of these sites the adsorption process became less efficient along time [13]. The adsorption efficiency for EtBr decreased with the increase in EtBr concentration (Fig. 2A). This is due to the fact that there are the same available binding sites for more molecules of EtBr.

Fig. 2. A: Effect of the contact time and initial EtBr concentration on EtBr adsorption by ACP and NP (adsorbent dose 8 g/L, pH = 8). B: Effect of solution pH on the adsorption of EtBr by ACP and NP ([EtBr] = 30 mg/L; adsorbent dose = 8 g/L and contact time 210 min). C: Effect of adsorbent dose on EtBr adsorption by ACP aNP ([EtBr] = 30 mg/L, pH = 8 and contact time 210 min). ACP: aluminium-coated pumice; NP: natural pumice.

namely adsorbent dose (8 g/L) and contact time (210 min). The adsorption efficiency increased with the increase in pH for both adsorbents (Fig. 2B). At low pH the H+ ions are predominant in the solution, so the surface of pumice will be charged with those ions. This will lead to a strong electrostatic repulsion between the positively charged pumice and the ethidium cations, thereby, decreasing the EtBr adsorption.

3.3. Effect of the initial pH on EtBr adsorption 3.4. Effect of the adsorbent dose on EtBr adsorption The pH of the solution is an important factor controlling the surface charge of the adsorbent and the adsorbate uptake. To determine the optimum pH for maximum EtBr adsorption, the equilibrium adsorption of EtBr (for an initial concentration of 30 mg/L) was investigated for a pH range from 3 to 8 while the other parameters were maintained constant,

The effect of the adsorbent dose on EtBr adsorption at a fixed pH of 8 and an initial EtBr concentration of 30 mg/L is shown in Fig. 2C. EtBr adsorption increased with the increase of the adsorbent dose because more adsorbent implies more available binding sites. The maximum

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adsorption capacity occurred for 8 g of adsorbent. Therefore, the EtBr adsorption increased with the increase in the adsorbent dose up to a certain extent. Thus, it can be observed from Fig. 2C that for amounts of adsorbent higher than 8 g, there was no significant change in the EtBr adsorption for both adsorbents. So, this amount of adsorbent was used in the subsequent studies.

Table 1 Langmuir and Freundlich constants and calculated and experimental values of the maximum adsorption capacities for different initial EtBr concentrations at pH 8, adsorbent dose 8 g/L and contact time 90 and 150 min for aluminium-coated pumice (ACP) and natural pumice (NP), respectively. Langmuir isotherm Adsorbent

EtBr (mg/L)

qe calc. (mg/g)

qe exp. (mg/g)

qm (mg/g)

b and RL (L/mg)

R2

ACP

10 30 50 100 10 30 50 100

0.09 ± 0.002 2.25 ± 0.04 4.56 ± 0.07 8.87 ± 0.06 0.04 ± 0.001 1.05 ± 0.05 2.12 ± 0.02 4.15 ± 0.18

0.09 ± 0.001 2.18 ± 0.03 4.30 ± 0.08 7.95 ± 0.09 0.04 ± 0.001 1.03 ± 0.05 2.05 ± 0.07 3.87 ± 0.10

76.92

0.05 0.28

0.29

58.82

0.12 0.16

0.64

3.5. Adsorption isotherms Equilibrium data, commonly known as adsorption isotherms, describe how the adsorbate interacts with the adsorbents and give a comprehensive understanding of the nature of the interaction. Several isotherm equations have been developed and used for such analysis, and among them, the isotherms of Langmuir and Freundlich, the most commonly used, were applied in this study. The Langmuir isotherm can be used to describe the physical adsorption on a single layer and it is expressed as [18]: q bC e : qe ¼ m 1 þ bC e

ð1Þ

NP

Freundlich isotherm Adsorbent

EtBr (mg/L)

qe calc. (mg/g)

qe exp. (mg/g)

Kf (mg/g)

n

R2

ACP

10 30 50 100 10 30 50 100

0.09 ± 0.002 2.25 ± 0.04 4.56 ± 0.07 8.87 ± 0.06 0.04 ± 0.001 1.05 ± 0.05 2.12 ± 0.02 4.15 ± 0.18

0.09 ± 0.001 2.18 ± 0.03 4.30 ± 0.08 7.95 ± 0.09 0.04 ± 0.001 1.03 ± 0.05 2.05 ± 0.07 3.87 ± 0.10

0.26

1.005

0.99

0.34

1.027

0.99

The linearized form of the above equation can be written as follows: ce 1 1 þ ¼ ce : qe qm b qm

ð2Þ

NP

The essential characteristic of the Langmuir isotherms can be expressed by a dimensionless constant RL, which is defined as: RL ¼ 1=ð1 þ b C0 Þ:

ð3Þ

The value of RL indicates the type of the isotherm: unfavourable (RL N 1), linear (RL = 1), favourable (0 b RL b 1) or irreversible (RL = 0) [25]. The Freundlich isotherm can be expressed in the following form [9]: qe ¼ K F ce 1=n

ð4Þ

and in its linearized form: logqe ¼ logk F þ

1 logce n

ð5Þ

where qm (mg/g) is the maximum adsorption capacity, ce (mg/L) is the EtBr concentration at equilibrium, b (L/mg) is the Langmuir constant, KF is the Freundlich constant and is related to the adsorption capacity at equilibrium and 1/n is the heterogeneity factor representing the intensity of adsorption. To determine the constants the linearized forms of the equations are used. The values of the constants (b and RL for Langmuir; KF and n for Freundlich), the experimental and the calculated qe (mg/g) values and the linear determination coefficient (R2) for Langmuir and Freundlich are given in Table 1. The adsorption of EtBr followed the Freundlich isotherm for both adsorbents, indicating a heterogeneous adsorbent surface. The experimental results also showed that ACP had a greater capacity to adsorb EtBr than NP (Table 1). Furthermore, the values of n between 1 b n b 10 mean favourable adsorption [14,15]. Comparing the maximum adsorption capacities of EtBr among various adsorbents, based on the Langmuir isotherm model, reported in the literature (Table S1, supplementary material), the ACP and NP adsorbents used in this study exhibited a much higher adsorption capacity than the other reported adsorbents (about 88–100 fold for ACP and 67–76 fold for NP). The adsorption capacity of NP (58.82 mg/g) was found to be increased by about 30% after coating (Table 1). This is likely due to the surface area was increased by 3-fold after aluminium coating. Thus, the surface area of pumice increased considerably (from 8 to 25 m2/g) after aluminium coating resulting in the formation of microporous structures (Fig. 1B).

3.6. Adsorption kinetics studies The adsorption kinetics describes the rate of solute adsorption on adsorbents which controls the equilibrium time and influences the adsorption mechanism. The pseudo-first order equation and pseudosecond order equation were applied to investigate the kinetics of the adsorption process. These kinetics models can be linearized as follows: Pseudo-first order equation:   q K1 t Log 1− t ¼ − qe 2:303

ð6Þ

Pseudo-second order equation: t 1 1 ¼ þ t qt K 2 qe 2 qe

ð7Þ

where qe (mg/g) and qt (mg/g) are the EtBr adsorbed at equilibrium and at a particular time, respectively, t is the time in min, K1 is the pseudofirst order rate constant (1/min) and K2 is the pseudo-second-order rate constant (g/mg min). The plots are depicted in Fig. 3A. The values of the constants, the experimental and the calculated qe (mg/g) values and the linear determination coefficient (R2) obtained for both equations are given in Table 2. The data fitted the pseudo-second order kinetics model (Fig. 3B) which indicated that chemisorption was the rate-controlling step. This agrees with the results reported by other researchers which showed that the pseudo-second-order model described adequately the kinetics of EtBr adsorption by other adsorbents [7,27]. 3.7. Intra-particle diffusion study The intra-particle diffusion model was used to identify the mechanism involved in the adsorption process. Considering that the adsorption rate is controlled by pore and intra-particle diffusion, in a nonflow-agitated system, according to Weber and Moris [31], the amount adsorbed (qt) is proportional to the square root of time (t1/2): qt ¼ kp t 1=2

ð8Þ

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Table 3 Intra-particle diffusion rate constants for different initial EtBr concentrations at pH 8. Adsorbent EtBr Kp,1 (mg/L) (mg/g min1/2) ACP NP

30 100 30 100

Kp,2 (mg/g min1/2)

0.242 ± 0.00026 0.147 ± 0.0001 0.809 ± 0.0005 0.555 ± 0.0003 0.242 ± 0.0002 0.182 ± 0.0001 0.606 ± 0.00036 0.6 ± 0.00041

Kp,3 (mg/g min1/2)

R2

0.039 ± 0.00002 0.023 ± 0.00001 0.005 ± 0.00003 0.028 ± 0.00002

0.737 0.808 0.588 0.672

steep part corresponds to the external surface adsorption or instantaneous adsorption stage, the second part is the gradual adsorption stage, where the intra-particle diffusion controls the adsorption rate, and the third part is the final equilibrium stage where the intraparticle diffusion begins to decrease due to the low concentration of the adsorbate in the bulk solution [5]. In Fig. 3C the plot of qt versus t1/2 is depicted. The value of the rate constants for each stage (kp,1, kp,2 and kp,3) are listed in Table 3. As shown in Fig. 3C two linear stages were involved in the EtBr adsorption by NP and ACP with a rapid diffusion rate in the initial stage at the highest EtBr concentration (i.e. 100 mg/L). The line did not pass through the origin (Fig. 3C), indicating that the intra-particle diffusion was not the only rate-controlling step of the adsorption process. Thus, the external mass transfer also controlled the adsorption process at the initial stages. This may be due to the differences in the mass transfer rate in the initial and final adsorption stages [5]. 3.8. Performance analysis of ANN training phases for NP and ACP

Fig. 3. Adsorption kinetics: (A) Pseudo-first order, (B) Pseudo-second order and (C) Intraparticle diffusion for EtBr adsorption by aluminium-coated pumice (ACP) and natural pumice (NP).

where qt (mg/g) is the adsorbate uptake at a time t (min) and kp (mg/g min1/2) is the intra-particle diffusion rate constant. The plot of qt versus t1/2 can show different lines [4] indicating that two or more stages are involved in the adsorption process. The first

The ANN model was used to predict the combined effects of initial pH, adsorbent dose, contact time, treatment type and initial EtBr concentration (input variables) on adsorption efficiency (output variable). The results showed that the proposed three-layer ANN model was able to predict the relationship with high accuracy as indicated by a very high determination coefficient (R2 = 0.983) and a low relative error (RE = 0.009). The validity of the prediction results was further investigated by introducing a new independent set of input variables that were not used in the training and validation of the developed ANN. The relative errors for testing and validation sets (0.037 and 0.126, respectively) were comparable to the training error (0.009), indicating that the developed ANN was able to predict new observations. A sensitivity analysis of the effect of various input variables on the adsorption efficiency showed that the contact time was the most influential variable with 47% weight, followed by the initial pH with 28% weight and the adsorbent dose with 16% weight, while the effect of the initial EtBr concentration and the adsorbent type (i.e., NP and ACP) was minimal with 3.4% and 6.2% weights, respectively. The predicted versus observed adsorption efficiencies are shown in Fig. 4, indicating a satisfactory agreement between the observed and the predicted adsorption efficiencies was obtained. Accordingly, the ANN methodology provided a useful tool for the prediction of the adsorption efficiency as a function of the input factors which may not be possible using other conventional modelling techniques.

Table 2 Parameters obtained from the kinetics equations. Pseudo-first kinetic model

Pseudo-second kinetic model

Adsorbent

EtBr (mg/L)

qe exp. (mg/g)

qe cal. (mg/g)

K1

R2

qe cal. (mg/g)

K2

R2

NP

30 100 30 100

5.2 ± 0.04 16.1 ± 0.07 5.76 ± 0.06 18.26 ± 0.02

3.78 ± 0.08 10.6 ± 0.04 3.15 ± 0.02 8.89 ± 0.09

0.0026 ± 0.0002 0.003 ± 0.0001 0.0034 ± 0.0001 0.0039 ± 0.0003

0.41 0.51 0.24 0.40

4.9 ± 0.091 16.95 ± 0.098 5.58 ± 0.014 17.85 ± 0.102

0.033 ± 0.0008 0.04 ± 0.0006 0.047 ± 0.0003 0.011 ± 0.0004

0.995 0.982 0.998 0.997

ACP

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Fig. 4. Predicted versus observed adsorption efficiency as predicted by a single hidden layer feed forward artificial neural network (ANN).

4. Conclusions The EtBr adsorption from aqueous solutions by natural pumice (NP) and natural pumice coated with aluminium (ACP) depended on contact time, initial pH of the solution, adsorbent dose and initial EtBr concentration. Based on the isotherm analysis, the adsorption data of both adsorbents were well described by the Freundlich isotherm model and the adsorption kinetics fitted the pseudo-second order kinetics model. The maximum adsorption capacity (qm) was 58.82 and 76.92 mg/g for NP and ACP, respectively. The adsorption process was controlled not only by the intra-particle diffusion but also by the external mass transfer. Furthermore, the proposed three-layer feed forward and back-propagated ANN model showed precise and effective predictions on adsorption efficiencies of EtBr from aqueous solutions for both NP and ACP taking into account the effect of initial pH, contact time, initial EtBr concentration, adsorbent dose and adsorbent type. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.molliq.2015.08.063. Acknowledgments The authors would like to thank Dr. Ashraf Aly Hassan from the Office of Research and Development, NRMRL, US Environmental Protection Agency, Cincinnati, OH, USA for his helpful suggestions regarding the modification methods. Also, authors are thankful to the Student Research Committee, Mazandaran University of Medical Sciences, Sari, Iran, for the financial support under the project grant 93-75. There is no conflict of interest declared by the authors. References [1] C. Adán, A. Bahamonde, A. Martínez-Arias, M. Fernández-García, L. Pérez-Estrada, S. Malato, Solar light assisted photodegradation of ethidium bromide over titaniabased catalysts, Catal. Today 129 (2007) 79–85. [2] C. Adán, A. Martínez-Arias, M. Fernández-García, A. Bahamonde, Photocatalytic degradation of ethidium bromide over titania in aqueous solutions, Appl. Catal. B Environ. 76 (2007) 395–402. [3] F. Akbal, Sorption of phenol and 4-chlorophenol onto pumice treated with cationic surfactant, J. Environ. Manag. 74 (2005) 239–244. [4] S.J. Allen, G. Mckey, K.Y.H. Khadur, Intraparticle diffusion of basic dye during adsorption on to sphagnum peat, Environ. Pollut. 56 (1989) 39–42. [5] M.S. Chiou, G.S. Chuang, Competitive adsorption of dye metanil yellow and RB15 in acid solutions on chemically cross-linked chitosan beads, Chemosphere 62 (2006) 731–740. [6] M.W. De Oliveira, A.W. Hilsdorf, A.F. de Souza Silva, A.F. Oliveira, Estudo da adsorção de brometo de etídeo em resina XAD-7, Quim. Nova 32 (2009) 1134–1138. [7] A. Fakhri, Assessment of ethidium bromide and ethidium monoazide bromide removal from aqueous matrices by adsorption on cupric oxide nanoparticles, Ecotoxicol. Environ. Saf. 104 (2014) 386–392. [8] L.B. Far, B. Souri, M. Heidari, R. Khoshnavazi, Evaluation of iron and manganesecoated pumice application for the removal of As(V) from aqueous solutions, Iran. J. Environ. Health 9 (2012) 1–9.

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Further reading [74] T.A. Saleh, V.K. Gupta, Adv. Colloid Interface Sci. 211 (2014) 92–100.

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