Experimental design and modeling of ultrasound assisted simultaneous adsorption of cationic dyes onto ZnS: Mn-NPs-AC from binary mixture

Accepted Manuscript Experimental design and modeling of ultrasound assisted simultaneous adsorption of cationic dyes onto ZnS: Mn -NPs-AC from binary mixture Arash Asfaram, Mehrorang Ghaedi, Fakhri Yousefi, Mehdi Dastkhoon PII: DOI: Reference:

S1350-4177(16)30114-6 http://dx.doi.org/10.1016/j.ultsonch.2016.04.016 ULTSON 3190

To appear in:

Ultrasonics Sonochemistry

Received Date: Revised Date: Accepted Date:

20 February 2016 11 April 2016 12 April 2016

Please cite this article as: A. Asfaram, M. Ghaedi, F. Yousefi, M. Dastkhoon, Experimental design and modeling of ultrasound assisted simultaneous adsorption of cationic dyes onto ZnS: Mn -NPs-AC from binary mixture, Ultrasonics Sonochemistry (2016), doi: http://dx.doi.org/10.1016/j.ultsonch.2016.04.016

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Experimental design and modeling of ultrasound assisted simultaneous adsorption of cationic dyes onto ZnS: Mn -NPs-AC from binary mixture Arash Asfaram, Mehrorang Ghaedi*, Fakhri Yousefi, Mehdi Dastkhoon

5 6

Chemistry Department, Yasouj University, Yasouj 75918-74831, Iran.

7 8

Abstract

9

The manganese impregnated zinc sulfide nanoparticles deposited on activated carbon (ZnS: Mn-NPs-AC) which

10

fully was synthesized and characterized sucsesfully applied for simultaneous removal of malachite green and

11

methylene blue in binary situation. The effects of variables such as pH (2.0-10.0), sonication time (1-5 min),

12

adsorbent mass (0.005-0.025 g) and MB and MG concentration (4-20 mg L-1) on their removal efficiency was

13

studied dy central composite design (CCD) to correlate dyes removal percentage to above mention variables that

14

guides amongst the maximum influence was seen by changing the sonication time and adsorbent mass.

15

Sonication time, adsorbent mass and pH in despite of dyes concentrations has positive relation with removal

16

percentage . Multiple regression analysis of the experimental results is associated with 3-D response surface and

17

contour plots that guide setting condition at pH of 7.0, 3 min sonication time, 0.025 g Mn: ZnS-NPs-AC and 15

18

mg L-1 of MB and MG lead to acheivment of removal efficiencies of 99.87 and 98.56% for MG and MB,

19

respectively. The pseudo-second-order model as best choice efficiency describe the dyes adsorption behaviour ,

20

while MG and MB maximum adsorption capacity according to Langmuir was 202.43 and 191.57 mg g-1.

21 22

Keywords: Binary; Central composite design; Malachite green; Methylene blue; ZnS: Mn-NPs-AC;

23

Ultrasound-assisted adsorption.

24 25

1.

26

The chemicals correspond to the waste of textiles, paper, rubber, plastics, leather, cosmetics, food and

27

pharmaceuticals following arrival to aqueous media led to generation of hazards and injuries to all living things

28

and organism and most prominent problem is attributed to presence of wide category of dyes which around 10–

29

15% of these dyes arrived to the different media [1, 2].

30

Dyes present in water system without preliminary treatment as a non-legal and desirable phenomena make an

31

urgent requirement to supply efficient low coast and ecofriendly protocol for removal of pollutants from water

32

resource to promote the quality of water following reducing pollutants and level to value lower than threshold

33

limit [3-5].

34

The conventional treatment methods like ion-exchange, electro dialysis, micro- and ultra-filtration, reverse

35

osmosis, oxidation, and solvent extraction are expensive and tedious with respect to adsorption [6-9]. The latter

36

protocol is very favorable method based on its simple, easy operation and high-performance efficiency

37

operations is strongly recommended to remove toxic substances. Carbon based adsorbent because of presence of

38

various functional group oppress structure is highly demand material which capable adsorption process for

39

efficient quantitative and safe removal of water polluted media. [10-12].

Introduction

* Corresponding author at: Tel (
): +98 741 2223048; fax: +98 741 2223048 (
). E-mail address: [email protected]; [email protected] (M. Ghaedi, )

1

40

Activated carbon (AC) as best and high abundant support has high demand and application for removal of

41

organic contaminants to regulate environmental quality [13-15].

42

Modification of AC surface via nano scale materials simultaneously led to appearance of more extra reactive

43

center (metallic or nonmetallic) which in combination to the enhancement of surface area and porosity which in

44

cooperation with AC functional group strongly led to progress in chemisorption and/or physisorption of various

45

compounds. The size, surface structure and intraparticle interaction of nanomaterials enhance their usability to

46

interact with other compounds [16-18].

47

Combination of above mention advantages with ultrasound application which accelerate mass transfer via

48

raising diffusion coefficient by best dispersion of adsorbent and also probability via opening the porosity of

49

adsorbent lead to remarkable enhance in efficiency of adsorption procedure [19, 20]. Roosta and coworkers [21]

50

pointed out the enhancement of adsorption rate of dyes onto ZnS:Ni nanoparticles loaded on AC. Our recent

51

research reveal that sonication lead to raising mass transfer coefficient through cavitation and acoustic streaming

52

to increase dyes removal [22].

53

Present study focus on simultaneous adsorption of MG and MB by ZnS: Mn-NPs-AC under ultrasound while

54

central composite design (CCD) combined with RSM using minimum number of experiments permit to

55

achieved useful information about interaction in main effect of variables like [23, 24]. pH, sonication time,

56

adsorbent mass and MG and MB concentration on the adsorption process to search and fine best operation

57

optimum conditions. Also, the effect of nanoparticles content on dyes adsorption process was investigated and

58

the kinetic and isotherm of adsorption were studied.

59 60

2.

61

2.1. Materials

62

Malachite green (MG) and methylene blue (MB) (Fig. 1a), zinc sulfate (ZnSO4), sodium sulphide (Na2S) and

63

manganese sulfate (MnSO4) were purchased from Sigma Aldrich Company. Activated carbon was purchased

64

from Merck, Darmstadt Company. All chemicals were used as received without further purification.

Experimental

65 66

2.2. Characterization of adsorbent

67

The prepared ZnS: Mn-NPs-AC were characterized by scanning electron microscope (SEM: KYKY-EM3200,

68

Hitachi Company, China), X-ray diffraction (XRD, Philips, PW1800, Eindhoven, Netherland) and energy-

69

dispersive X-ray spectrometer (EDX) methods. Surface functional groups were detected using the Fourier

70

Transform Infrared (FT-IR)(Perkin-Elmer spectrum, RX-IFTIR, USA) using a KBr wafer with the wave number

71

ranging 400–4000 cm-1. The concentration of MG and MB was determined at proper wavelengths using UV-

72

Visible spectrophotometer (Lambda 25 UV-Vis spectrometer from Perkin-Elmer Instruments, Wellesley,

73

Massachusetts, USA) according to calibration curve obtained from first-order derivatives of the spectra. The pH

74

values of the solutions were adjusted from 2.0–10.0 by adding either HCl and/or NaOH solution by pH-meter,

75

Metrohm 686, Switzerland. Ultrasonic device (TECNO-GAZ, Parma, Italy) equipped with digital timer and

76

temperature controller was used for the sonication. A Hermle centrifuge (Hermle-Labortechnik 2206A,

77

Gosheimerstr, Germany) was used to accelerate the phase separation.

78 79

2.3. Preparation of adsorbent

2

80

The typical procedure for preparation of ZnS: Mn nanoparticles (ZnS: Mn-NPs) loaded on activated carbon

81

(AC) was mention as following: 100 mL of 0.2 and 0.05 mol L-1 of zinc sulfate and manganese sulfate solution,

82

respectively was diluted by 90 mL deionized water and subsequently, was mixed thoroughly following drop

83

wise addition of 50 mL of 0.5 mol L-1 sodium sulphide solution. The obtain homogeneous mixture was allowed

84

to stand for 24 h at 70 °C. Addition of about 10 g AC dispersed in 200 mL deionized water to above suspension

85

in Erlenmeyer flask is associated with efficient deposition of procedure nano structure onto the AC and in the

86

later stage this composite was dried in hot air oven at 80 °C for 2 h. After 3 h, this composite following filtering

87

and washing were used in removal process.

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2.4. Ultrasound assisted adsorption procedure

90

The under study dyes ultrasound assisted adsorption on to the present adsorbent is conducted as follow: 50 mL

91

of 15 mg L-1 MB and MG solution at pH 7.0 was mixed completely with 0.025 g of the adsorbent and it

92

subsequent exposure under ultrasound at the frequency of 40 kHz (3 min) led to best dispersion of adsorbent in

93

to the solution which raising temperature simultaneously led to enhancement coefficient and progress in the

94

mass transfer. For the adsorption of MB and MG, was added in At each experimental point, 10 mL of samples

95

were drawn out and immediately centrifuged and MB and MG content according to calibration curve obtain at

96

derivative spectrophotometric method at 348.2 and 625 nm, respectively was quantified. Blank experiments

97

(without any adsorbent) were run to investigate the possible degradation of the dyes studied in presence of

98

ultrasonication. No dyes degradation was observed.

99

The adsorption isotherms was investigated over different initial dyes concentration in the range of 4 to 20 mg L-

100

1

101

stage the dyes removal percentage and their subsequent adsorption capacity was calculated according to

102

equations presented in our present publication [25, 26]. The sorption kinetics was studied over sonication times

103

were (0.5-7 min) at optimum conditions. In addition to the coefficient of determination (R2), the chi-square (χ2)

104

test methods were also used to evaluate the best-fit of the model to the experimental data using Eq. (1).

at optimum specified conditions: temperature of 25 ºC, pH value of 7.0 and a sonication time of 3 min. in all

105 n

106

χ =∑ 2

i=1

(q

e,exp

- q e,cal )

2

(1)

q e,cal

107 108

where n is the number of data points, qe,exp is the observation from the experiment, and qe,cal is the calculation

109

from the models. The smaller function values point out the best curve fitting.

110 111

2.5. Experimental design

112

At first, the effect of changing a single factor (pH, sonication time, adsorbent mass and dyes concentration) on

113

the yield of total dyes adsorption was employed to determine their preliminary range to study and attain the

114

numerical value of main and variables interaction following analysis of results by RSM under CCD. According

115

to Table 1, viz. pH (2.0-10.0), initial dye concentrations (4-20 mg L-1), sonication time (1-5 min) and adsorbent

116

mass (0.005-0.025 g). It is known that analysis and experimental data passible to achieved quadratic equation

117

which efficiently able to predict real behavior of adsorption system and also represent relation among response

118

to significant term and its efficiency was judged based on least-squares regression [27]:

3

119

The analysis of variance (ANOVA) was performed to justify the significance and adequacy of the developed

120

regression model. The adequacy of the response surface models were evaluated by calculation of the

121

determination coefficient (R2), coefficient of variation, adequate precision and also by testing it for the lack of

122

fit.

123 124

3.

125

3.1. Derivative spectrophotometry for predication of simultaneous adsorption of MB and MG in binary

126

Results and discussion

system

127

The absorption spectra of MG and MB (Fig. 1a) show presence of considerable overlap among their spectrum

128

that reveal failure of their direct UV–Vis absorption spectra analysis to estimate their acurate and precies

129

determination in their mixture system [26]. This overlap as one of main problem can be resolved by derevative

130

spectroscopy. Therfore, the first order derivates of the spectra (Fig. 1b, c) show that MB can be determined at

131

348.2 nm in the presence of MG with approximatly zero absorbance at this region. At 625 nm MG (Fig 1d)

132

accuratly with minimum interfrence correspond to MB can be quantified. The calibration equations for the two

133

dyes were constructed by plotting the absolute values of the first-order derivative signal (dA/dλ) at 384.2 and

134

625 nm for MB and MG, respectively. The concentration of each dye could be calculated from the calibration

135

graphs.

136 137

3.2. Characterization of the adsorbent

138

The surface morphology and the size of the prepared Mn doped ZnS-NPs (SEM, Fig. 2a) reveal the spherical

139

shape of nanoparticles with the approximately diameter of 20-80 nm, while its chemical composition study by

140

EDS spectrum of reveal presence of C, Zn, S and Mn in adsorbent (Fig.2b). The atomic ratios of C, Zn, S and

141

Mn in adsorbent are 88.20%, 3.47%, 5.80 and 2.53%, respectively.

142

The XRD analysis of ZnS: Mn-NPs-AC at various diffraction angle 2θ from 10 to 90º (Fig. 2c) is composed of

143

three strong XRD peaks at 2θ = 28.70, 48.08 and 56.43 assigned to lattice planes of (111), (220) and (311)

144

which strongly support cubic structure of sphalerite ZnS: Mn nanoparticles, respectively (JCPDS, No.05-0566).

145

The Mn peak was observed at (200) plane and exhibits orthorhombic structure of ZnS and Mn doped ZnS

146

(JCPDS, NO. 24-0733). The nanocrystal size of the prepared ZnS: Mn-NPs was estimated about 56 nm based on

147

Debeye-Scherrer for full width at half-maximum (FWHM) of the (111) peak [28].

148

FTIR spectra of ZnS and Mn doped ZnS-NPs are showed in Fig. 2d. The characteristic ZnS vibration peaks can

149

be observed at 1120.84, 618.43, and 463.11 cm-1. The obtained peak values has good agreement with the

150

literature [29]. The broad absorption peak in the range of 3000-3600 cm-1 is correspond to –OH group and

151

indicate the existence of water absorbed in the surface of nanoparticles.

152

The FTIR spectrum of Mn doped ZnS-NPs shows peaks similar to pure ZnS particle. The peak at 1120.84 cm-1

153

split into two peaks, i.e. at 1121.26 and 1132.53 cm-1, indicating that the doped Mn affected the structure of

154

portion of the ZnS particles. The bands at 1120-1133 cm-1 is corresponded to bond of metal-S bonds and such as

155

Zn–S–Mn.

156 157

3.3. Statistical analysis

158

The whole design matrix together with their experimental responses (Table 1) was analyzed to construct a

159

quadratic model. The responses were correlated with the three variables studied by multiple regression analysis

4

160

using the second-order polynomial. The coefficients of the model equation and their statistical significance were

161

evaluated using Design-Expert 9.0.5. In this study, insignificant terms (limited influence) were excluded in each

162

stage to improve the model efficiency. According, the quadratic regression models for predication of MG and

163

MB removal in terms of coded factors are expressed as follows:

164 165

R%MB = +40.43+ 3.21X1 +12.30X2 +1889.3X3 + 54.23X1X3 - 0.12X1X5 + 67.91X2 X3 + 0.52 X2 X4 + 1.60X3X4 - 0.10X4X5 - 0.30X12 - 2.7X22 - 74500X32

(2)

166 167

R%MG = -29.13 + 7.70X1 + 20.90X 2 + 4255.1X 3 - 2.6X 4 - 0.11X1X 2 123.74X1X 3 + 0.58X 2 X 4 -15.01X 3X 5 - 0.40X12 - 3.0X 22 - 34344.5X 23 - 0.11X 24

(3)

168 169

where X1, X2, X3, X4 and X5 are the coded values of the pH, sonication time, adsorbent mass, MB and MG

170

concentration, respectively. Positive values in these equations indicate that these terms increase the response and

171

the negative values decrease the response.

172

The results of analysis of variance (ANOVA) indicate that the contribution of the quadratic models was very

173

significant (p < 0.0001). The non-significant lack of fit of responses (Table 2) were sufficiently explained by the

174

regression equations. F-values of 188.40 and 149.86 with p values less than 0.0001 simultaneously proof

175

statistically significance of model.

176

The sequential effect of the factors was explained by Pareto chart (Fig. 3a, b) and the most important factors

177

which reveal that effective adsorption parameter were sonication time, adsorbent mass, pH, MB and MG

178

concentration. Sonication intensifies the chemical reaction by generating cavitation and shear forces but

179

pretreatment conditions such as adsorbent mass, pH and dye concentration are also important for dyes removal.

180

The determination coefficient (R2) of Eqs. (2, 3) were 0.9971 and 0.9963 which means that 99.71% and 99.63%

181

of the variation was attributed to the independent variable. Moreover, the correlation coefficients (Adj-R2) of the

182

above equations were 0.9918 and 0.9879 that suggest extensive and very acceptable correlation between the

183

independent variables [26]. Fig. 3c and d demonstrate this correlation between predicted and experimental

184

values. Moreover, the values of coefficient of variation (CV) were 0.6843 and 0.7981 for MB and MG,

185

respectively. CV value generally express the standard deviation as percentage of the mean and as is known its

186

lower value is proportional to better reproducibility [30].

187

Adequate precision is a signal-to noise ratio means that range of predicted values at fixed levels to average

188

prediction error, while value higher than 4 also confirm efficiency and acceptable results concern to model and

189

in present research [31] its value was 51.292 and 44.553 for MB and MG, respectively.

190

One of the most important assumptions for statistical analysis of experiments data is their normal distribution

191

(Fig. 3e, f) [32] that means achievement of straight line relationship of plot [33]. The residual values explain the

192

difference between predicted values (model) and the observed values (experimental) [17] and it was seen that

193

points were reasonably aligned suggesting normal distribution. All the points of normal probability plots were

194

found to fall in the range of -2 to +3 and -3 to +3 for MB and MG, respectively.

195 196

3.4. Effect of process variables on dyes adsorption

5

197

Sonication time (agitation pathway) achieves better contact between the dye and adsorbent mass particles. The

198

higher values of MB removal were obtained by simultaneous increasing agitation time and adsorbent mass (Fig.

199

4a). There is no need for higher adsorbent mass and sonication time to achieved maximum MB removal

200

efficiency. Increasing the adsorbent mass at longer time of agitation probably leads to adsorbent particles

201

aggregation and creating screening effect. Such aggregation would refer to decrease in the total surface area of

202

the adsorbent and cause reduction in MB removal efficiency. On the other hand, economical aspect encourage

203

that the lower agitation time and adsorbent mass are good. The maximum MB removal presented in this paper

204

was found to be at 3 min sonication.

205

According to plot of pH versus sonication time (Fig. 4b) it can seen that over pH range of 2.0–10.0, the effect of

206

sonication time on the adsorption was almost constant (65%), while shifting pH to alkali region lead to

207

enhancement in adsorption regardless of sonication time. A 99.50% removal was observed when the pH and

208

sonication time were found to be 7.0 and 4 min, respectively. Achievement of maximum dyes removal at higher

209

pH indicate high contribution of charge and nature of adsorbent surface on adsorption process. At negative

210

surface charge, the interaction between surface (negative charge) and positive charge MG is greater and

211

probably via electrostatic interactions strongly adsorbed dyes molecular. Also, lower dye removal at acidic pH is

212

probably due to the presence of excess H+ ions which compete with MG for adsorption sites of sorbents.

213

Fig. 4c show the effect of MB concentration on its removal (R %) at fixed value of adsorbent mass and pH

214

(0.015 g and 6.0), respectively. As shown, increasing initial MB concentration from 4 to 20 mg L-1 is associated

215

with diminished in R% within contact time of 2 min, while at lower initial dye concentrations all dye molecules

216

adsorbed onto adsorptive sites and higher R% of MB was seen. The lower removal percentage at higher dye

217

concentrations is due to the saturation of adsorption sites of the adsorbent.

218

As shown in Fig. 4 it can be concluded that maximum removal of MB and MG could be achieved when the

219

sonication time was increased. The rapid adsorption show the efficiency of ultrasound power in terms of usage

220

in wastewater treatment. The results showed that the initial adsorption rate is very rapid because of high

221

available surface area and vacant site of adsorbent due to dispersion of adsorbent into solution by ultrasonic

222

power.

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3.5. Optimization of ultrasonic conditions for adsorption of MG and MB

225

According to the software optimization step, the desired goal for each operational condition (pH, sonication

226

time, adsorbent mas, MB and MG concentration) was selected. The responses (MG and MB removal) were

227

defined as maximum to achieve the highest performance. The value of desirability obtained (0.9998) shows that

228

the estimated function may represent the experimental model and desired conditions. The optimum adsorption is

229

related to following conditions, pH of 7.0, 3 min sonication, 0.025 g adsorbent and 15 mg L-1 of MB and MG

230

that predicted 100.00% and 99.34% for their removal percentage, respectively. Under the optimum conditions,

231

the experimental yield of MG was 99.87±0.92 (N = 5) and MB was 98.56 ± 1.52 (N = 5), which were close to

232

the predicted values and support that predicated optimum point really guide us to achieve best operational

233

response using at least number of runs (consumption of reagents) in short acceptable time.

234 235

3.6. Adsorption isotherm

6

236

The distribution of MG and MB between the liquid phase and the solid adsorbent phase can be expressed by

237

most popular models namely Langmuir and Freundlich which are axplained and understaned according to their

238

well known assumption and phenomena [34]. The equation is described in the following equation [35]:

239 240

Ce C 1 = + e q e Q max K L Q max

(4)

241 242

where qe is the solid phase adsorbate concentration in equilibrium (mg g-1), Qmax the maximum adsorption

243

capacity corresponding to complete monolayer coverage on the surface (mg g-1), Ce the concentration of

244

adsorbate at equilibrium (mg L−1) and KL is the Langmuir constant (L mg-1). Eq. (4).

245

The Langmuir isotherm constants KL and Qmax were calculated fromthe slope and intercept of the plot between

246

Ce/qe and Ce (Fig. 5a).

247

The essential characteristics of the Langmuir equation can be expressed in term of a dimensionless separation

248

factor (RL) defined as [36]:

249 250

RL =

1 1 + K LC0

(5)

251 252

where RL is the equilibrium constant it indicates the type of adsorption, The RL values between 0 and 1 indicate

253

the favorable adsorption.

254

On the other hand, the Freundlich equation is an empirical equation based on adsorption on a heterogeneous

255

surface. The equation is commonly represented by [37]:

256 257

1 log qe = log K F + log Ce n

(6)

258 259

Ce is the equilibrium concentration of dye (mg L-1). The values of KF and 1/n obtained from the intercept and

260

slope of the plot of log qe versus log Ce, (Fig. 5b) with the chi-square (χ2) test at all adsorbent masss are shown

261

in Table 4.

262

The equilibrium isotherm for the adsorption of dyes on adsorbent was determined. The Qmax, KL, KF, 1/n, R2

263

(correlation coefficient) and chi-square (χ2) are given in Table 4. As seen in Table 4, the maximum adsorption

264

capacities for MG and MB onto ZnS: Mn-NPs-AC were found to be 202.429 and 191.570 mg g-1, respectively.

265

The result confirm that the maximum adsorption capacity is highly depend on dyes chemical structure and size.

266

The Langmuir model was found to be the most appropriate to describe the adsorption process of these cationic

267

dyes on ZnS: Mn-NPs-AC. This suggests that a monolayer adsorption process occurs on the homogeneous

268

distribution of active sites onto adsorbent surface and this has been reported [26, 38, 39].

269

The separation parameter (RL) was found to be: 0.0586 to 0.2798 (for MB), and 0.0045 to 0.0281 (for MG) for

270

the initial dyes concentration of 4–20 mg L-1 which are within the range of 0–1 indicates thier favorable

271

adsorption onto adsorbent. The 1/n numerical value lower than unity strongly support favorable by adsorbent at

272

all adsorbent mass studied.

7

273 274

3.7. Adsorption kinetics

275

The adsorption rate is fitted into two kinetic equations to determine a suitable kinetics model. The wo equations

276

are Pseud ofirst and second-order. The Pseudo-first-order is described in the following equation [32]:

277 278

log (q eq - q t ) = log q eq -

k1t 2.303

(7)

279 280

where qeq is the amount of solute adsorbed at equilibrium per unit mass of adsorbent (mg g-1), qt is the amount of

281

solute adsorbed at any given time t. k1 is the rate constant (1.min-1). The value for k1 is calculated from the slope

282

of the linear plot of log(qeq−qt) versus time (Fig. 5c).

283

On the other hand, the Ho’s second-order rate equation, which has been called a pseudo-second order kinetic

284

expression, has also been applied widely [40] and described by Eq. (8). For this case, it was convenient to plot

285

the experimental data as t/qt against t, which shows a linear tendency of the data and allows for the

286

determination of the adsorption rate constant, namely K2, in a simple way.

287 288

t 1 1 = + t 2 q t k 2 q eq q eq

(8)

289 290

where K2 is the second-order reaction rate equilibrium constant (g· mg–1· min–1). A linear plot of t/qt strongly

291

indicate ability of the second-order kinetic for well presentation of exprimenatl data (Fig. 5d).

292

All the determined model parameters and constants with the statistical analysis values (Table 4) show that low

293

R2 beside high χ2 values low agreement among thoretical & experimental qe the pseudo-first-order show its

294

failure display that the model was not favorable for defining the biosorption kinetics. Contrary to this model, the

295

relatively high R2 as well as small χ2 values for the pseudo-second-order model assert that the adsorption process

296

obeyed the pseudo-second-order model kinetics at all initial dye concentrations. In addition, the calculated qe

297

values are in good agreement with the experimentally obtained qe values, which confirms that the adsorption of

298

dyes onto adsorbent surface follows pseudo second order reaction. It was also found that the pseudo second

299

order rate constant (k2) reduced with increasing dye concentration in solution indicating reduction of adsorption

300

rate. This is presumably due to the rapid growing of surface positive charge for initial rapid uptake of dye on

301

adsorbent surface, which inhibits the dye uptake at the later stages of reaction by columbic repulsion. All these

302

indicated that ZnS: Mn-NPs-AC could be used to remove dye efficiently from dilute solutions with monolayer

303

mechanism.

304 305 306

3.8. Performance comparison of ultrasound technology for the removal of dyes by different methods and adsorbents

307

The adsorption capacity of MB and MG were compared with those of other adsorbents using contact time. The

308

maximum monolayer coverage from Langmuir model as magnitude of the efficiency of an adsorbent (Table 5)

309

show that its value for ZnS: Mn-NPs-AC is higher than that of other mention adsorbents. The present adsorbent

310

due to its high surface area has high capacity of adsorption. It may be seen (Table 5) that the contact time for

8

311

proposed method in comparison with all adsorbents are preferable and superior and shows satisfactory removal

312

performance for MG and MB. To better understand the advantage of ultrasound technology, its adsorption

313

performance was compared with magnetic stirrer methods. We conducted a comparative study between this

314

effect and the effect of mechanical agitation to evaluate the effect of ultrasonication on the dye adsorption (Fig.

315

6). The experimental results confirm that ultrasound assisted adsorption process need required around 25 fold

316

lower than the magnetic-stirring-assisted at 25 ºC to obtain dyes adsorption with similar yields that may be

317

related to its remarkable ability to improve contact area and diffusion coeficient which improve method

318

efficiency. The ultrasonic-assisted enhancement of removal could be attributed to the high-pressure shock waves

319

and high-speed microjets during the violent collapse of cavitation bubbles.

320

Also, the monolayer saturation capacity (Table 5) at equilibrium Q m in the presence of ultrasound assisted

321

adsorption was greater than that in the absence of ultrasound assisted adsorption (202.429, 191.570 mg g-1 and

322

55.126 and 41.368 mg g-1 for MG and MB, respectively). This was attributed to cavitation effects which

323

increased capability of the porous particle structure for dyes adsorption and/or the appearance of new sites of

324

sorption by disruption of sorbent particles.

325 326

4.

327

A multi-response optimization study based on CCD allow searching optimum conditions to achive the best and

328

maximum MG and MB adsorption onto ZnS: Mn-NPs-AC by the aid of ultrasound. Combination of RSM with

329

CCD guide us that sonication, adsorbent mass and pH have significant effect on dyes adsorption. Values of

330

“Prob > F” less than 0.0001 indicate model terms have significant effect on adsorption of MG and MB.

331

Maximum simultaneous dyes removal (> 98.50) was obtained at pH 7.0, 0.025 g adsorbent mass, 15 mg L-1 of

332

MB and MG at 3 min sonication. The Pareto chart results enunciated that the significance of the parameters is as

333

follows (the most to the least significant): sonication time>adsorbent mass > pH > initial dye concentrations.

334

Adsorption kinetics including the pseudo-first and second order kinetic models were researched and the data

335

fitted better with the pseudo-second order kinetic model (R2 = 0.997). For adsorption isotherms, Langmuir

336

isotherm was proved to be the best correlation (R2= 0.997) compared with the Freundlich isotherms. The

337

mechanism of under study dyes adsorption under ultrasound assisted irradiation show and proof great potential

338

application of sonication for treatment of dyes.

Conclusion

339 340

Acknowledgment

341

The authors grateful from the Iranian National Sciences Foundation (INSF for grant number of 92039361) and

342

Research Council of the University of Yasouj for their financial support.

343 344 345 346 347 348 349 350 351 9

352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407

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622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 14

638 639 640 641 642 643 644 645 646 647

Table. 1. Experimental design matrix using RSM model. Factors Lowest (-α) Low (-1) X1: pH 2.0 4.0 X2: sonication time (min) 1 2 X3: adsorbent mass (g) 0.005 0.01 X4: MB concentration (mg L-1) 4 8 X5: MG concentration (mg L-1) 4 8 Factors Run Space type X1 X2 X3 1 Axial 6.0 3 0.020 2 Factorial 8.0 4 0.025 3 Factorial 4.0 2 0.015 4 Factorial 8.0 4 0.015 5 Center 6.0 3 0.020 6 Factorial 8.0 4 0.025 7 Factorial 8.0 2 0.025 8 Center 6.0 3 0.020 9 Axial 6.0 1 0.020 10 Axial 10.0 3 0.020 11 Factorial 4.0 4 0.015 12 Factorial 4.0 4 0.015 13 Axial 6.0 3 0.010 14 Axial 6.0 3 0.020 15 Factorial 8.0 2 0.025 16 Center 6.0 3 0.020 17 Center 6.0 3 0.020 18 Factorial 8.0 2 0.015 19 Factorial 4.0 4 0.025 20 Axial 6.0 3 0.020 21 Factorial 4.0 2 0.025 22 Axial 2.0 3 0.020 23 Center 6.0 3 0.020 24 Factorial 8.0 2 0.015 25 Factorial 4.0 2 0.025 26 Factorial 4.0 2 0.015 27 Axial 6.0 3 0.030 28 Factorial 4.0 4 0.025 29 Center 6.0 3 0.020 30 Factorial 8.0 4 0.015 31 Axial 6.0 5 0.020 32 Axial 6.0 3 0.020

648 649 15

Levels Central (0) 6.0 3 0.015 12 12 X4 20 16 16 8 12 8 8 12 12 12 16 8 12 12 16 12 12 8 8 12 16 12 12 16 8 8 12 16 12 16 12 4

X5 12 16 8 16 12 8 16 12 12 12 16 8 12 4 8 12 12 8 16 20 16 12 12 16 8 16 12 8 12 8 12 12

High (+1) 8.0 4 0.020 16 16

Highest (+α) 10.0 5 0.025 20 20 Response R% MB R% MG 91.507 88.125 97.250 97.800 83.930 76.920 92.100 92.680 95.720 96.450 94.245 97.850 86.340 91.650 95.670 95.980 73.240 73.360 87.550 92.800 97.450 95.992 93.390 88.120 86.700 89.020 95.800 96.600 79.160 80.200 96.580 96.030 95.000 97.200 84.960 83.570 98.739 95.100 96.130 97.200 83.920 81.250 93.950 87.700 96.800 97.500 74.120 85.600 84.400 90.660 88.900 77.950 90.250 97.410 96.900 97.850 96.380 97.200 96.850 99.570 96.920 95.700 95.230 91.070

655

656 657 658 659 660

Table. 2. Analysis of variance (ANOVA) and estimated regression coefficients for R% of MB and MG Source of Dfa MB MG variation SS b MS c F-value P-value SS MS F-value Model 20 1465.1 73.254 188.4 < 0.0001 1591.9 79.595 149.86 X1 1 52.227 52.227 134.32 < 0.0001 51.856 51.856 97.636 X2 1 919.51 919.51 2364.8 < 0.0001 838.3 838.3 1578.4 X3 1 11.144 11.144 28.66 0.000233 98.975 98.975 186.35 X4 1 18.2700 18.2700 46.988 < 0.0001 2.8621 2.8621 5.3889 X5 1 1.3273 1.3273 3.4136 0.091709 0.8370 0.8370 1.576 X1X2 1 6.9380 6.9380 17.8430 0.001426 0.7234 0.7234 1.362 X1X3 1 4.7046 4.7046 12.0990 0.005163 24.4980 24.4980 46.125 X1X4 1 3.0941 3.0941 7.9575 0.016641 0.4768 0.4768 0.89772 X1X5 1 15.5910 15.5910 40.0970 < 0.0001 6.0001 6.0001 11.297 X2X3 1 1.8455 1.8455 4.7464 0.05199 3.4988 3.4988 6.5876 X2X4 1 69.9150 69.9150 179.8100 < 0.0001 87.0580 87.0580 163.92 X2X5 1 0.6906 0.6906 1.7760 0.20959 2.9912 2.9912 5.6319 X3X4 1 0.0160 0.0160 0.0412 0.84294 71.9190 71.9190 135.41 X3X5 1 20.4850 20.4850 52.6840 < 0.0001 1.4412 1.4412 2.7135 X4X5 1 10.8640 10.8640 27.9400 0.000258 4.9751 4.9751 9.3674 X12 1 49.0980 49.0980 126.2700 < 0.0001 75.0810 75.0810 141.36 X22 1 215.6300 215.6300 554.5600 < 0.0001 269.2800 269.2800 507.01 X32 1 101.7500 101.7500 261.7000 < 0.0001 21.6250 21.6250 40.716 X42 1 11.9820 11.9820 30.8160 0.000172 91.1720 91.1720 171.66 X52 1 0.0029 0.0029 0.0075 0.93235 0.1151 0.1151 0.21668 Residual 29 4.2771 0.3888 5.8422 0.5311 Lack of Fit 22 1.9727 0.3288 0.7134 0.65704 3.6767 0.6128 1.4149 Pure Error 7 2.3043 0.4609 2.1655 0.43311 Cor Total 49 1469.4000 1597.7000

Model Summary Statistics Response SD CV R2 Adj-R2 R% MB 0.6236 0.6843 0.9971 0.9918 R% MG 0.7288 0.7981 0.9963 0.9897 a b c Degree of freedom Sum of square Mean square d Standard deviation

Pred-R2 0.9620 0.9365 e Coefficient of variation

17

P-value < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.040478 0.23536 0.26788 < 0.0001 0.36374 0.006351 0.026207 < 0.0001 0.036945 < 0.0001 0.12774 0.01084 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.65066

Regression coefficients MB MG +40.43 -29.13 +3.21 +7.67 +12.23 +20.90 +1889.30 +4255.10 -0.09 -2.64 -0.12 -0.30 +0.33 -0.11 +54.23 -123.74 -0.06 -0.02 -0.12 +0.08 +67.93 -93.53 +0.52 +0.58 +0.05 -0.11 +1.58 -106.01 +56.58 -15.01 -0.05 +0.04 -0.32 -0.40 -2.71 -3.03 -74500 -34344.5 -0.04 -0.11 +0.001 +0.004

0.3602

AP 51.2915 44.5528 f Adequate precision

PRESS 55.8455 101.4900 g predicted residual sum of square

661

Table 3. Isotherm constant parameters and correlation coefficients calculated for the adsorption of dyes onto ZnS: Mn-NPs-AC in binary component system. Value parameters Isotherm Parameters MB MG 0.005 g 0.015 g 0.025 g 0.005 g 0.015 g 0.025 g 191.570 85.0340 77.340 202.429 102.354 82.713 Qm (mg.g-1) 0.6436 0.8038 0.7152 10.978 2.0743 0.6404 KL (L mg-1) Langmuir R2 0.9994 0.9977 0.9998 0.9974 0.9995 0.9992 RL 0.0721-0.2798 0.0586-0.2372 0.0653-0.2590 0.0045-0.0223 0.0235-0.1076 0.07242-0.281 χ2 0.0054 0.0954 0.0072 0.0774 0.0603 0.0480 1/n 0.5332 0.6091 0.7436 0.3762 0.6576 0.7624 Freundlich 6.262 4.681 4.527 10.256 6.721 4.532 KF (L mg-1) R2 0.9644 0.9825 0.9944 0.9209 0.9839 0.9948 χ2 2.2763 1.203 1.0762 2.873 1.348 0.837

662

18

663 664

665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708

Table 4. Kinetic parameters for the adsorption of dyes using 0.025 g ZnS: Mn-NPs-AC 20 mg L-1 of each dye in binary component system. Value parameters Model Dye MB Concentration (mg L-1) 10 15 20 10 k1 (min-1) 0.0141 0.0184 0.0244 0.0110 First-orderqe (calc) (mg g-1) 1.153 7.972 15.837 6.298 kinetic R2 0.9322 0.9717 0.6924 0.8925 χ2 5.2363 4.8721 6.8032 5.3620 Pseudok2 (g· mg–1· min–1) 0.0024 0.0011 0.0007 0.0024 second-order- qe (calc) (mg g-1) 20.408 30.301 37.636 20.576 kinetic R2 0.9999 0.9997 0.9999 0.9992 χ2 0.0411 0.0378 0.0119 0.0324 Experimental qe (exp) (mg g-1) 19.842 29.265 36.734 19.903

Table. 5. Comparison for the removal of dyes by different methods and adsorbents.

19

as well as 10, 15 and MG 15 0.0159 13.797 0.9197 3.9050 0.0010 31.446 0.9993 0.0284 29.983

20 0.0204 17.123 0.8518 7.8110 0.0006 40.651 0.9978 0.0487 38.570

Adsorbent

dye

Method

Chitosan bead Tin oxide-NPs-AC Coconut coir AC Walnut shell (WS) AC-CoFe2O4 composites Graphene oxide Graphite oxide/polyurethane (GO/PU) Sodium alginate-coated Fe3O4-NPs activated sintering process red mud ZnO–NR–AC Zn(OH)2-NP-AC MWCNT Aerobic granules Yarrowia lipolytica ISF7 Nanocrystalline ZnO doped lanthanide oxide Semiconductor metal oxide nanocatalyst BDD electrodes and Na2SO4 (electrolyte) HKUST-1 MOF and SBA-15 Activated carbon (walnut shells) Graphene PDA microspheres MWCNTs filled with Fe2O3 particles Humic Acid-coated Fe3O 4-NP Ag NPs-AC CuO-NP-AC ZnO–NR–AC Cu2O-NP-AC ZnS: Cu-NP-AC Nanocoated electrode Fe0 aggregate Co doped Ti/TiO2 nanotube/PbO2 anodes NiMnFe2O4 nanoparticles Fe3O 4/reduced graphene oxide

MG MG MG MG MG MG MG MG MG MG MG MG MG MG MG MG MG MG MG MB MB MB MB MB MB MB MB MB MB MB MB MB MB MB

Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Ultrasound assisted adsorption Ultrasound assisted adsorption Ultrasound assisted adsorption Biosorption Biosorption Degradation Sonocatalytic degradation ultrasonic-assisted ozone oxidation Electrochemical degradation Photocatalytic degradation Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Magnetic-stirring-assisted adsorption Ultrasound assisted adsorption Ultrasound assisted adsorption Ultrasound assisted adsorption Ultrasound electrochemical degradation Ultrasound enhanced advanced Fenton Electrocatalytic degradation Photocatalytic degradation Heterogeneous Fenton degradation Ultrasound assisted adsorption a Magnetic-stirring-assisted adsorption a Ultrasound assisted adsorption a Magnetic-stirring-assisted adsorption a

ZnS: Mn-NPs-AC

MG MB

709 710 711

Sorption capacity (mg g-1 ) 93.55 142.87 27.44 90.8 89.3 30.090 68.82 47.84 336.4 59.17 74.63 57.6 56.80 155.098 315.0 153.85 90.7 42.9 93.08 71.43 10.55 89.29 110.0 51.70 202.429 55.126 191.570 41.368

Contact time (min) 300 30 30 60 80 1440 180 20 180 4 8.6 2.6 120 1440 30 90 10 180 80 1440 255 100 60 27 15 15 4.0 4.0 3.0 60 10 120 300 60 3.0

Experimental conditions: initial dye concentration:4-20 mg L-1, adsorbent mass:0.005 g, V=50 mL, pH: 7.0, contact time: 3 min, Tempertaue: 25 ºC. a

20

Ref. [41] [42] [43] [44] [45] [46] [47] [48] [49] [17] [50] [51] [52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [38] [66] [67] [68] [69] [70] [71] [72] This work

712 713 714

Fig. 1. (a) Zero order derivative spectra for MB, MG and binary mixture, (b), (c) and (d) first order derivative spectra for MB, MG and binary mixture.

21

715 716

Fig. 2. (a) FESEM image, (d) EDS analysis of the ZnS: Mn-NPs-AC, (c) XRD pattern and (d) FT-IR spectrum of the ZnS: Mn-NPs.

22

717 718 719 720 721 722 723 724 725

Fig. 3. a, b) Standardized Pareto chart showing the effect of different factor terms on dyes adsorption values. Bars exceeding the vertical line on the graph indicate that the corresponding factor terms are significant (p<0.05), plot showing model predicted value versus actual value (c, d) and Normal probability plots of residuals for R% of (e) MB and (f) MG.

23

726 727 728 729 730 731

Fig. 4. 3D surface and contour plots indicating interaction effects of independent variables on variation of R%: (a) sonication time–adsorbent mass (MB); (b) pH-sonication time (MG) and (c) sonication time–MB concentration (MB).

24

732 733 734 735

Fig. 5. (a) Langmuir (b) Freundlich (c) Pseudo-first and (d) second-order kinetic plots of the MB or MG dyes onto Mn: ZnS -NPs-AC (initial dye concentration:15 mg L-1, adsorbent mass:0.025 g, V=50 mL, pH: 7.0, Tempertaue: 25 ºC).

25

736 737 738 739 740

Fig. 6. Comparison between conventional stirring magnetically and ultrasound-assisted process curve. Experimental conditions: initial dye concentration:15 mg L-1, adsorbent mass:0.025 g, V=50 mL, pH: 7.0, Tempertaue: 25 ºC.

741

26

742 743 744 745 746 747 748

Highlights ZnS: Mn-NPs-AC were used for the simultaneous removal of dyes from aqueous solution. Derivative spectrophotometric method for resolve of spectra overlap of MG and MB. Dyes removal significantly were accelerated under application of ultrasound. Response surface methodology was used to optimize the process variables. The MB and MG adsorption data were best followed by Langmuir and pseudo-second order models.

27