1

Optimization of photocatalytic degradation of sulfonated diazo dye

C.I. Reactive Green 19 using ceramic coated TiO2 nanoparticles

Masoud Rastegar Farajzadeha, Kamran Rahmati Shadbada, Alireza Khataeeb,Reza Pourrajaba

a Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz,

Iran

b Water and Wastewater Company of East Azerbaijan, Tabriz, Iran

* Corresponding author:

Tel.: +98 411 3393165;

Fax: +98 411 3340191;

E-mail: a_khataee@tabrizu.ac.ir

2

Abstract

Optimization of photocatalytic degradation of C.I. Reactive Green 19 (RG19)

under UV light irradiation using ceramic coated TiO2 nanoparticles in a rectangular

photoreactor was studied. The investigated TiO2 was Millennium PC–500 (crystallites

mean size 8 nm) immobilized on ceramic plates. Central composite design was used

for optimization of UV/TiO2 process. Predicted values of decolorization efficiency

were found to be in good agreement with experimental values (R2= 0.97 and Adj–R2=

0.91). Optimization results showed that maximum decolorization efficiency was

achieved at the optimum conditions: initial dye concentration 10 mg/L, UV light

intensity 47.2 W/m2, flow rate 150 mL/min and reaction time 240 min. Photocatalytic

mineralization of RG19 was monitored by chemical oxygen demand (COD) decrease

and changes in UV–Vis spectrum.

Keywords: TiO2 nanoparticles; Photodegradation; Immobilization; Ceramic plates;

Response surface methodology;

3

1. Introduction

A well-flourished textile industrial development has originated increasing use of a

wide variety of synthetic dyes and pollution by wastewater contaminated with

dyestuff is becoming increasingly alarming worldwide [1, 2]. The azo dyes make up

about a half of all known dyestuffs in the world, making them the largest group of

synthetic colorants released into the environment [3]. Reactive group of azo dyes are

mostly used in textile dyeing due to their superior fastness to the applied fabric, high

photolytic stability and recalcitrant to microbial degradation. However, reactive dyes

exhibit low levels of fixation with the fiber and up to 10–50% of total dye used in

dyeing process remain left in the spent dye bath [4, 5]. C.I. Reactive Green 19 (RG19)

is an examples of reactive dyes bearing two azo groups as the chromophoric moiety

and two chlorotriazine groups as reactive groups (see Table 1). Many synthetic azo

dyes and their metabolites are toxic, carcinogenic, mutagenic, leading to potential

health hazard to humankind [4]. In general, the treatment of dye–containing effluents

is being undertaken by biological, adsorption, membrane, coagulation–flocculation,

oxidation–ozonation, and Advanced Oxidation Processes (AOPs) [6]. AOPs have

been developed to degrade the nonbiodegradable contaminants of drinking water and

industrial effluents into harmless species (e.g. CO2, H2O, etc). Heterogeneous

photocatalysis via combination of TiO2 and UV light is considered one of the most

promising AOPs for destruction of water–soluble organic pollutants [7, 8].

In the literature reports, the most widely used photocatalytic process is carried out

in a batch slurry photoreactor operating with titanium dioxide suspensions [9-11].

However, slurry reactors have a number of practical and economical disadvantages

which were mentioned in our previous works [12, 13]. In order to solve these

problems, supported photocatalysts have been developed. The most important

4

properties of a suitable support are to be chemically inert, to present a high specific

surface area and to be transparent to UV radiation [14]. In this work, ceramic plates

were used as a support of TiO2 nanoparticles.

In the conventional methods used to determine the influence of operational

parameters, experiments were carried out varying systematically the studied

parameter and keeping constant the others. This should be repeated for all the

influencing parameters, resulting in an unreliable number of experiments. Response

surface methodology (RSM) is a statistical method being useful for the optimization of

chemical reactions and/or industrial processes and widely used for experimental design

[15]. RSM also quantifies the relationship between the controllable input parameters and

the obtained response surfaces [16]. Process optimization by RSM is faster for gathering

experimental research results than the rather conventional, time consuming one-factor-at-

a-time approach [17].

In our previous works, we have studied optimization of photocatalytic degradation

of acidic and basic dyes using TiO2 nanoparticles immobilized on non–woven papers

and glass plates [12, 13]. In this work, ceramic plates were used as a support of TiO2

nanoparticles. The central composite design (CCD) was applied to the optimization of

photocatalytic treatment of RG19 solution on immobilized TiO2 nanoparticles in a

rectangular photocatalytic reactor. The factors (variables) investigated were the

reaction time, initial dye concentration, flow rate and UV light intensity. Chemical

oxygen demand removal was monitored in order to explain the mineralization of the

dye in the UV/TiO2 process.

5

2. Experimental

2.1. Materials

The investigated photocatalyst was Millennium TiO2 PC–500 (anatase>99%,

crystallites mean size 8 nm) immobilized on ceramic plates. These characteristics

were proved by our XRD and TEM analyses [13]. BET surface area and total pore

volume of the used photocatalyst were 320.76 m2/g and 0.3104 cm3/g, respectively.

The dye, C.I. Reactive Green 19, was obtained from Alvan Sabet Company, Iran. Its

chemical structure and characteristics are given in Table 1. Methanol,

methyltrimethoxysilane (MTMOS) and HCl were obtained from Merck Co., Germany.

2.2. Immobilization of TiO2 nanoparticles on ceramic plates

TiO2 nanoparticles were fixed on ceramic plates by sol-gel dip-coating method.

Organically modified silica (Ormosil) was used as hydrophobic binder. The coating

solution contained 23 mL of methanol, 0.264 g of TiO2 nanoparticles, and appropriate

amount of methyltrimethoxysilane (MTMOS), which was set to achieve the desired

TiO2/Ormosil weight ratio. HCl solution (1 N) was added for hydrolysis of MTMOS.

The suspension was sonicated for 15 min by a Sonoplus Ultrasonic Homogenizer HD

2200, Germany. The main advantage of this kind of films is that they have good

mechanical anchoring due to the chemical bonding, in comparison with films made

with the dried mixture of TiO2 and water. Figure 1 shows the SEM images of the

ceramic plate before and after immobilization of TiO2 nanoparticles on it. As shown

in Figure 1, the entire surface of the ceramic plate has been coated with TiO2

nanoparticles. The coated plates were thoroughly washed with distilled water for the

removal of free TiO2 particles.

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2.3. Photocatalysis experiments

The experimental set–up is based on a rectangular photocatalytic reactor with

workable of area 15 × 90 cm2, made out of stainless steel that its schematic diagram

and function were explained previously . 2000 mL solutions of RG19 were degraded in

all cases. Artificial irradiation was provided by three 30 W UV–C lamps (Philips, The

Netherlands) with peak intensity at 254 nm, positioned above the reactor. The lamps

were turned on at the beginning of each experiment. The distance between the

solution and the UV source was adjusted according to the experimental conditions. On

the surface of the solution, the radiation intensity was measured by a UV radiometer

purchased from Cassy Lab Company, Germany. At different reaction times obtained

with experimental design, 2 mL sample were taken and the remaining RG19 was

determined using a spectrophotometer at λmax= 630 nm and calibration curve. Using

this method, the percentage of color removal could be obtained. The percent color

removal (CR (%)) was expressed as the percentage ratio of decolorized dye

concentration to that of the initial one.

2.4. Analytical procedures

The photocatalytic reactions were monitored by UV–Vis spectrophotometer (WPA

lightwave S2000, England) in the range of 200–700 nm. Scanning electron

microscopy (SEM) was carried out on a Hitachi SEM (Model S–4160, USA) device

after gold–plating of the samples. Nitrogen sorption analyses were obtained with a

(Micrometrics, Gimini series) sorptometer using standard continuous procedures at

77.15 K on calcined samples that had been degassed at 363 K for one hour and then at

403 K under high vacuum for at least 10 hours. The surface area was calculated

7

according to the Brunauer–Emmett–Teller (BET) model over a relative pressure range

of 0.05–0.90.

2.5. Experimental design

In the present study central composite design was employed for optimization of

photocatalytic decolorization process. In order to evaluate the influence of operating

parameters on the photocatalytic decolorization efficiency of RG19, four main factors

were chosen: initial dye concentration (mg/L) (X1), UV light intensity (W/m2) (X2), flow

rate (mL/min) (X3) and reaction time (min) (X4). A total of 31 experiments were

employed in this work, including 24=16 cube points, 7 replications at the center point and

8 axial points. Experimental data were analyzed using Minitab 15 software. For statistical

calculations, the variables Xi were coded as xi according to the following equation:

XXXx 0i

i

(1)

where X0 is the value of Xi at the center point and X presents the step change [18, 19].

The experimental ranges and the levels of the independent variables for RG19 removal

are given in Table 2. It should be mentioned that preliminary experiments were performed

to determine the extreme values of the variables.

3. Results and discussion

3.1. CCD model and analysis of variance (ANOVA) study

The 4–factor CCD matrix and experimental results obtained in the photocatalytic

decolorization runs are presented in Table 3. The second–order polynomial response

equation (Eq. (2)) was used to correlate the dependent and independent variables.

8

Y=b0 + b1x1 + b2x2 + b3x3 + b4x4 + b12x1x2 + b13x1x3 + b14x1x4 + b23x2x3 + b24x2x4 +

b34x3x4 + b1121x + b22

22x + b33

23x + b44

24x (2)

where Y is the response variable of decolorization efficiency. The bis are regression

coefficients for linear effects; bik the regression coefficients for quadratic effects and xi

represent coded experimental levels of the variables.

Based on these results, an empirical relationship between the response and

independent variables was attained and expressed by the following second–order

polynomial equation:

Y=42.005–9.681x1+3.880x2+1.835x3+9.024x4+4.482x1x2–0.561x1x3–1.128x1x4–

0.806x2x3+3.591x2x4–1.968x3x4–0.543 21x +0.381 2

2x +0.301 23x –1.163 2

4x (3)

Photocatalytic decolorization efficiencies (CR (%)) have been predicted by Eq. (3)

and presented in Table 3. These results indicated good agreements between the

experimental and predicted values of decolorization efficiency. The regression coefficient

(R2) quantitatively evaluates the correlation between the experimental data and the

predicted responses. Experimental results and the predicted values obtained using model

(Eq. (3)) are shown in Figure 2. As can be seen, the predicted values match the

experimental values reasonably well with R2 of 0.97. This implies that 97% of the

variations for percent color removal are explained by the independent variables and this

also means that the model does not explain only about 3% of variation. Adjusted R2 (Adj–

R2) is also a measure of goodness of a fit, but it is more suitable for comparing models

with different numbers of independent variables. It corrects R2–value for the sample size

and the number of terms in the model by using the degrees of freedom on its

computations [20]. Here, Adj–R2 value (0.91) was very close to the corresponding R2

value (see Table 3).

9

Table 4 indicates the results of the quadratic response surface model fitting in the

form of analysis of variance (ANOVA). ANOVA is required to test the significance and

adequacy of the model [20]. ANOVA subdivides the total variation of the results in two

components: variation associated with the model and variation associated with the

experimental error, showing whether the variation from the model is significant or not

when compared with the ones associated with residual error [21]. This comparison is

performed by F–value, which is the ratio between the mean square of the model and the

residual error. If the model is a good predictor of the experimental results, F–value should

be greater than the tabulated value of F–distribution for a certain number of degrees of

freedom in the model at a level of significance α. F–value obtained, 23.91, is clearly

greater than the tabulated F (2.352 at 95% significance) confirming the adequacy of the

model fits.

The student's t distribution and the corresponding values, along with the parameter

estimate, are given in Table 5. The P–values were used as a tool to check the significance

of each of the coefficients, which in turn, are necessary to understand the pattern of the

mutual interactions between the test variables. The larger the magnitude of the student's t–

test and smaller P–value, the more significant is the corresponding coefficient [22].

3.2. Effect of variables as response surface and counter plots

To study the effect of initial dye concentration on photocatalytic decolorization

efficiency, the experiments were carried out with initial dye concentration varying

from 2 to 18 mg/L at constant flow rate (150 mL/min) and UV light intensity (29.7

W/m2). The results have been displayed in Figure 3. This figure shows that the

photocatalytic decolorization efficiency slightly decreases with an increase in the

initial amount of RG19. This may be attributed to several factors. At high dye

10

concentration, the adsorbed dye molecules may occupy all the active sites of TiO2

surface and this leads to decrease in degradation efficiency. It means that as the

concentration of the dye increases, more and more molecules of the dye get adsorbed

on the surface of the photocatalyst. Therefore, requirement of the reactive species

(OH and O2–) needed for the degradation of the dye also increase. However, the

formation of OH and O2– on the catalyst surface remains constant for a given light

intensity, catalyst amount and duration of irradiation. Hence, the available hydroxyl

radicals are inadequate for the degradation of the dye at high concentrations.

Consequently, the degradation efficiency of the dye decreases as the concentration

increases [23, 24]. In addition an increase in substrate concentration can lead to the

generation of intermediates, which may adsorb on the surface of the catalyst. Slow

diffusion of the generated intermediates from the catalyst surface can result in the

deactivation of active sites of the photocatalyst and consequently, a reduction in the

degradation efficiency. In contrast, at low concentration, the number of the catalytic

sites will not be limiting factor and the rate of degradation is proportional to the

substrate concentration [10]. Another reason may be due to the absorption of light

photon by the dye itself leading to a lesser availability of photons for hydroxyl radical

generation [11].

Figure 4 illustrates the effect of UV light intensity and reaction time on

photocatalytic decolorization efficiency for initial dye concentration of 10 mg/L and

flow rate of 150 mL/min. As it is obvious from Figure 4, decolorization efficiency

increased with increasing UV light intensity and reaction time. The reason of this

observation is thought to be the fact that UV light intensity determines the extent of

light absorption by the photocatalyst to form electron–hole pairs which results in the

11

overall pollutant conversion. In other words, higher light intensity provides higher

energy for more TiO2 nanoparticles to produce electron–hole pairs [25].

Figure 5 shows the response surface and contour plots of photocatalytic

decolorization efficiency as a function of flow rate and reaction time. A slightly

improvement in photocatalytic decolorization efficiency with increasing flow rate has

been observed. The presumed reason is that when the dye solution flow rate is

increased, the turbulence in the system is enhanced. It may lead to decompose more

and more adsorbed dye molecules on the surface of TiO2 thus photocatalytic

decolorization efficiency increases. The high decolorization efficiencies at high flow

rates were also attributed to the increase in the mass transfer coefficient [26]. The

finding is in agreement with literature reports where higher photocatalytic efficiency

would result in higher flow rates [27-29]. As an instance, Mehrotra et al. [27]

explained the effect of circulation flow rates on photocatalytic degradation rate of

benzoic acid. They found that if the external mass transfer resistance existed, the

reaction rate would depend on the circulation flow rate (Q), particularly when

circulation flow rate was low. The external mass transfer resistance can be reduced to

a minimum by increasing mixing of fluid through stirring or increasing the circulating

flow rate (Reynolds number, Re) of the reaction medium.

3.3. Photocatalytic mineralization of RG19 at optimal conditions

Contour and surface plots for dye removal have been shown in Figures 3–5. Such

three-dimensional surfaces could give accurate geometrical representation and provide

useful information about the behavior of the system within the experimental design [30].

It is clear from these plots that the dye removal is favorable at optimum values. The

optimum values of the process variables for the maximum decolorization efficiency were

12

10 mg/L, 47.2 W/m2, 150 mL/min and 240 min for initial dye concentration (X1), UV

light intensity (X2), flow rate (X3) and reaction time (X4), respectively. At these optimum

values, the predicted and observed CR (%) was 70.35 and 69.36%, respectively.

Optimum values of the variables are given in Table 6. Photocatalytic mineralization of

the dye in UV/TiO2 process studied with 20 mg/L of RG19 solution at the achieved

optimum conditions. Mineralization was monitored by COD and changes in UV−Vis

spectrum.

COD is the measurement of the amount of oxygen in water consumed for

chemical oxidation total concentration of organics in the solution and the decrease of

COD reflects the degree of mineralization at the end of the photocatalytic process.

COD disappearance of RG19 has been depicted in Figure 6. The results indicated that

more than 92% COD removal was achieved under optimized conditions at the

irradiation time of 9 h. It implies that the strategy to optimize the decolorization

conditions and to obtain the maximal degradation efficiency by RSM for photocatalytic

decolorization of RG19 in this study is successful.

The changes in the UV–Vis spectra of RG19 during the photocatalytic process at

different irradiation times are shown in Figure 7. The dye carries two azo groups as

the chromophoric moiety and two chlorotriazine groups as reactive groups in different

sites on the molecule. The color of RG19 is the result of chromospheres such as azo

functional group and the two substituted aromatic species. The decrease of the

absorption peak of the dye at the maximum absorption wavelength (630 nm) in Figure

7 indicates photocatalytic destruction of RG19. The decrease is also meaningful with

respect to the nitrogen-to-nitrogen double bond (–N=N–) in RG19, as the most active

site for oxidative attack. Decrease in absorption intensity of the band at λmax during

13

the irradiation also expresses the loss of conjugation, especially the cleavage near the

azo bond of the organic molecule.

The weak band at 342-430 nm could be attributed to the *n and π-π*

transition related to the aromatic ring attached to the –N=N– group and –N=N– in the

dye molecule. The band at 224 nm could be attributed to the * and

*n transitions related to the aromatic rings attached to the azo group and also to a

secondary amine in the dye molecule. Absorbance decrease at 261–297 nm indicates

the degradation of aromatic part of the dye molecule [31].

Conclusions

The photocatalytic treatment of a textile dye RG19, from aqueous solution in a

rectangular photocatalytic reactor was optimized. Effect of operational parameters on

the decolorization efficiency of RG19 was evaluated by the response surface and

contour plots. The optimum values of initial dye concentration, UV light intensity,

flow rate and reaction time were 10 mg/L, 47.2 W/m2, 150 mL/min, 240 min,

respectively. Analysis of variance showed a high coefficient of determination (R2=

0.97 and Adj–R2= 0.91), thus ensuring a satisfactory adjustment of the second order

regression model with the experimental data. The results of COD decrease and UV–

Vis spectrum changes indicated that photocatalytic process could be used for

complete decolorization and mineralization of RG19.

Acknowledgements

The authors thank the Water and Wastewater Company of East Azerbaijan for

financial supports. The authors also wish to thank the University of Tabriz for all the

supports provided.

14

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17

Figure captions

Figure 1. Scanning electron microscopy images of TiO2 nanoparticles (a) before and

(b) after deposition on ceramic plates.

Figure 2. Comparison of the experimental results of photooxidative destruction

efficiency with those calculated via central composite design resulted equation.

Figure 3. The response surface and contour plots of photocatalytic decolorization

efficiency (%) as the function of initial dye concentration (mg/L) and reaction time

(min).

Figure 4. The response surface and contour plots of photocatalytic decolorization

efficiency (%) as the function of reaction time (min) and UV light intensity (W/m2).

Figure 5. The response surface and contour plots of photocatalytic decolorization

efficiency (%) as the function of flow rate (mL/min) and reaction time (min).

Figure 6. COD changes during photocatalytic degradation of RG19 at the optimized

conditions.

Figure 7. The changes in the absorption spectra of RG19 during the photocatalytic

process at different irradiation times under the optimized conditions.

.

18

Figures

(a)

(b)

Figure 1

19

y = 0.9995x + 0.0882R2 = 0.97

0

10

20

30

40

50

60

70

80

90

0 10 20 30 40 50 60 70 80 90

CR (%) Predicted

CR

(%) E

xper

imen

tal

Figure 2

20

Figure 3

21

Figure 4

22

Figure 5

23

050

100150200250300350400450

CO

D (m

g/L)

0 180 300 420 540

Time (min)

Figure 6

24

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

100 200 300 400 500 600 700 800

Wavelength (nm)

Abs

orba

nce

0 h

12 h

Figure 7

25

Tables

Table 1. Structure and characteristics of RG19.

Color indexname C.I. Rective Green 19 (RG19)

Chemicalstructure

Chemicalclass Anionic, Diazo

Molecularformula C40H23Cl2N15O19S6.6Na

Color indexnumber 68110-31-6

λmax (nm) 630

Mw (g/mol) 1418.92

26

Table 2. Experimental ranges and levels of the independent test variables.

Variables Ranges and levels

–2 –1 0 +1 +2Initial dye concentration (mg/L) (X1)UV light intensity (W/m2) (X2)Flow rate (mL/min) (X3)Reaction time (min) (X4)

212.25040

620.9510090

1029.70150140

1438.45200190

1847.20250240

27

Table 3. Analysis of variance (ANOVA) for fit of decolorization efficiency from centralcomposite design.

Source ofvariations

Sum ofsquares

Degree offreedom

AdjustedMean square F–value

RegressionResidualsTotal

5649.44270.04

5919.48

1416

403.5316.88

23.91

R2= 0.97, Adj–R2= 0.91

28

Table 4. Estimated regression coefficients and corresponding t and P values from thedata of central composite design experiments.

Coefficient Parameterestimate

Standarderror

t–value P–value

b0b1b2b3b4b12b13b14b23b24b34b11b22b33b44

42.005–9.6813.8801.8359.0244.482

–0.561–1.128–0.8063.591

–1.968–0.5430.3810.301

–1.163

1.5520.8380.8380.8380.8380.7680.7680.7680.7681.0271.0271.0271.0271.0271.027

27.052–11.545

4.6282.189

10.7615.835–0.730–1.469–1.0493.497

–1.917–0.5290.3710.293

–1.133

0.0000.0000.0000.0440.0000.0000.4760.1610.3100.0030.0730.6040.7150.7730.274

29

Table 5. Optimum operating conditions of the process variables.

Variable Optimum valueRG19

Initial dye concentration (mg/L) (X1)UV light intensity (W/m2) (X2)Flow rate (mL/min) (X3)Reaction time (min) (X4)Experimental CR(%)Predicted CR(%)

1047.2150240

69.3670.35