Quantitative structure–property relationship (QSPR) study for the degradation of dye wastewater by Mo–Zn–Al–O catalyst

Journal of Molecular Liquids 215 (2016) 461–466

Contents lists available at ScienceDirect

Journal of Molecular Liquids journal homepage: www.elsevier.com/locate/molliq

Quantitative structure–property relationship (QSPR) study for the degradation of dye wastewater by Mo–Zn–Al–O catalyst Yin Xu a,⁎, Xiao-ying Chen a, Yang Li a, Fei Ge a, Run-liang Zhu b,⁎ a b

Department of Environmental Science and Engineering, Xiangtan University, Xiangtan, Hunan 411105, PR China Guangzhou Institutes of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China

a r t i c l e

i n f o

Article history: Received 19 June 2015 Received in revised form 30 December 2015 Accepted 8 January 2016 Available online xxxx Keywords: Mo–Zn–Al–O catalyst Dyes QSPR DFT PLS

a b s t r a c t Quantitative structure–property relationships (QSPR) between the structure of dyes and the discoloration rate of dyes were established to predict the discoloration rate of dyes in catalytic wet air oxidation (CWAO) process. The DFT-based quantum chemical descriptors were obtained at the B3LYP/6-31G (d,p) level and partial lease squares (PLS) regression was employed for QSPR model development. The discoloration rate of dyes was recorded in the CWAO process by Mo–Zn–Al–O catalyst under room conditions. The optimal QSPR model with a cross-validation Q2(cum) value of 0.845 and R value of 0.9893 indicates that the QSPR model has sufficient predictive ability and robustness. Two components were selected in the model, which account for 0.733 of the variance of the predictor variables and 0.979 of the variance of the dependent variable. The absolute values of W*[1] for that absolute hardness (η) and the most negative atomic net charges of the molecule (q ) were 0.806657 and 0.561769 demonstrates that η and q− are the main causes for the first component. The second PLS component is loaded on descriptor dipole moment (μ) for which the W*[2] values were 0.906712. The equation was obtained with the descriptors: Y =51.5042+ 252.644η+ 0.899607μ+ 102.427q−(Y is the discoloration rate of dyes). The obtained QSPR model could be used for predicting the discoloration rate of dyes and also reveals our previous suspicion of catalytic mechanism. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Over the past few decades, dyes are widely used for color products in industries such as textiles, leather, tanneries, paper, rubber and plastics [1–3]. Due to their chemical structure, dyes have been classified into three groups as cationic dye, anionic dye and nonionic dye [4]. It is estimated that over 10,000 kinds of dyes are synthesized and more than 70,000 tons of dyes are discharged in effluent from textile and associated industries in the world each year [5,6]. Unfortunately, synthetic dyes are stable colorants that are even potentially carcinogenic, toxic and their release into the environment causes serious environmental and health problems. The removal of dyes from these effluents is an essential task for environment protection [7]. As one of the advanced oxidation processes, catalytic wet air oxidation (CWAO) has been widely used for the degradation of organic pollutants. The organic pollutants are oxidized by activated O2 species in the presence of a solid catalyst into carbon dioxide and water, or alternatively into easily biodegradable by-products [8,9]. An increasing number of studies have been focused on easing the harsh condition of CWAO. In our previous papers, the Mo–Zn–Al–O catalyst has been proved to degrade cationic red GTL and cationic orchid X-BL under ⁎ Corresponding authors. E-mail address: [email protected] (Y. Xu).

http://dx.doi.org/10.1016/j.molliq.2016.01.029 0167-7322/© 2016 Elsevier B.V. All rights reserved.

room temperature and atmospheric pressure [10,11]. However, if we measure accurately the discoloration rate of all dyes by the Mo–Zn– Al–O catalyst, it would be very time-consuming and complex. Thus, it is of importance to develop a model between dye structure and the degradation efficiency. Briefly, quantitative structure–property relationship (QSPR) is a promising method applied to quantify the relationship between molecular structural information and the physicochemical properties of chemicals. It can be applied for the prediction of properties of new compounds once a correlation between structural information and chemical properties is established. Also it can provide insight into the main factors that influence physico-chemical properties of chemicals [12–15]. In the past decades, many studies have presided on the application of QSPR/QSAR method to investigate the relationship between the structure of dyes and their affinity, fastness, absorption maximum, solubility, and toxicity [16–20]. However, there are few details on the relationship between the dye structure and the dye discoloration rate by the catalyst. Therefore, it is appropriate to use the chemical parameter of dyes to develop a high quality QSPR model for discoloration rate of dyes degraded by Mo–Zn–Al–O catalyst. In our study, the molecular structural parameters of dyes were calculated by the density function theory (DFT) and partial lease squares (PLS) regression was employed for QSPR model development. Wet air oxidation by the Mo–Zn–Al–O catalyst under room conditions was used to record the discoloration rate of dyes. Altogether, the

462

Y. Xu et al. / Journal of Molecular Liquids 215 (2016) 461–466

objective of this study is to develop a QSPR model between the dye structure and the discoloration rate of dye. Furthermore, the QSPR model will be used to predict the discoloration rate of various dyes degraded by Mo–Zn–Al–O catalyst.

cooled to room temperature and the resulting solid was denoted as the Mo–Zn–Al–O catalyst.

2. Materials and methods

The dyes were degraded by Mo–Zn–Al–O catalyst under room temperature and atmospheric pressure for 60 min through catalytic wet air oxidation. The catalyst amount of 1 g was used to degrade a 1 L solution of different dyes with a concentration of 100 mg/L. In the CWAO process, the solution was aerated by silver lake SP-780 aeration equipment at a rate of 3.5 L/min. The discoloration rate of dyes was estimated on the basis of the absorbance using a UV–vis spectrophotometer (Japan shimadzu UV 2550). Three replicates were performed for each experiment. The discoloration rate of each dye has been calculated by the average of the data.

2.1. Chemical The following dyes have been studied: cationic red GTL (C 20 H28 ClN5 O 6S); Methyl orange (C 14 H14 N3 SO 3 Na); acid red 8 (C 18 H14 N2 Na2 O 7 S2 ); Methyl red (C 15 H15N 3 O2 ); crystal violet (C 25 H30 N3 Cl·9H2O); indigo blue (C 16 H10 N2 O 2); basic red 9 (C 19 H17 N3 ·HCl); and rhodamine B (C 28 H31ClN 2 O3 ). Molecular structures and CAS numbers for the eight dyes included in this study are shown in Table 1.

2.3. CWAO experiments

2.4. Descriptors generation 2.2. Mo–Zn–Al–O catalyst preparation The Mo–Zn–Al–O catalyst was prepared using the co-precipitation and impregnation method [10], Firstly, co-precipitation was used to prepare Zn–Al-LDH with Zn(NO3)2·6H2O and Al(NO3)3·9H2O at pH 9.5–10. The Mo–Zn–Al–O catalyst was prepared by impregnation, then 20 g Zn–Al-LDH was added into 100 mL 0.28 mol/L ammonium heptamolybdate aqueous solution. The mixture was filtered and washed several times with deionized water to remove suspended materials after maintenance at 55 °C for 12 h. The resulting product calcined at 400 °C for 1 h after it was dried at 80 °C for 10 h. At last they were

Molecular structures of eight dyes were constructed with the software CS Chem3D Ultra 10.0 (ChemOffice 2006, CambridgeSoft Corporation). To save computational time, the MM+ force fields of the molecular mechanics (MM) were used to optimize initial geometry. Then, the resulting geometries were re-optimized using quantum chemical calculations with the DFT(B3LYP/6-31G(d,p)) level by the Gaussian 03 program [21] and correlation functional of Lee, Yang and Parr (B3LYP) [22,23]. A total of 24 molecular descriptors were obtained and two physico-chemical descriptors including molecular weight (Mw) and maximum absorption wavelength (λmax) of dyes were used

Table 1 CAS number and structures of the dyes under study. There are 8 dyes listed in Table 1. No

Dyes

IUPAC name

CAS No.

1

Cationic red GTL

[2-[[4-[(2-chloro-4-nitrophenyl)azo] phenyl]ethylamino]ethyl] trimethylammonium

14097-03-01

2

Methyl orange

4-[[4-(dimethylamino)phenyl]azo]-benzenesulfonicacisodiumsalt

547-58-0

3

Acid red 8

Disodium3-((2,4-dimethylphenyl)azo)-4-hydroxynaphthalene-2,7-disulphonate

4787-93-3

4

Methyl red

2-((4-(dimethylamino)phenyl)azo)-benzoicaci

493-52-7

5

Crystal violet

Ammonium,(4-(bis(p-(dimethylamino)phenyl)methylene)-2,5-cyclohexadien-1-yliden

548-62-9

6

Indigo blue

2-(1,3-dihydro-3-oxo-2 h-indol-2-ylidene)-1,2-dihydro-3 h-indol-3-on

482-89-3

7

Basic red 9

4-((4-aminophenyl)(4-imino-2,5-cyclohexadien-1-ylidene)methyl)-benzenaminm

569-61-9

8

Rhodamine B

9-(2-carboxyphenyl)-3,6-bis(diethylamino)-xanthyliuchloride

81-88-9

Structures

Y. Xu et al. / Journal of Molecular Liquids 215 (2016) 461–466

in the QSPR study. According to Koopmans' theorem for closed-shell molecules, absolute hardness (η), total dipole moment (μ), electronegativity (χ), electrophilicity index (Ω), the average molecular polarizability (α) and the average traceless quadrupole (Qii) were defined as follows: ðELOMO ‐EHOMO Þ ðIP−EAÞ η¼ ¼ 2 2 μ¼

ð1Þ

ðEHOMO þ ELUMO Þ ðIP þ EAÞ ¼‐ 2 2

ð2Þ

ðEHOMO þ ELUMO Þ ðIP þ EAÞ ¼ 2 2

ð3Þ

χ¼‐

IP ¼ ‐EHOMO and EA ¼ ‐ELUMO

ð4Þ

where EHOMO is the energy of the highest occupied molecular orbital, and ELUMO is the energy of the lowest unoccupied molecular orbital. These descriptors have been used in QSPR model for different properties. The electrophilicity index (Ω) as a function of the square of electronegativity divided by hardness is: ω¼

μ2 : 2η

ð5Þ

The average molecular polarizability (α) is defined as: α¼

ðαxx þ αyy þ αzzÞ 3

ð6Þ

where αxx, αyy, and αzz are the diagonal elements (x-, y-, and z-) of the molecular polarizability tensor. The average traceless quadrupole (Qii) reflects the degree of the asymmetry of the charge spherical distribution in the molecule and is defined as:

463

Table 2 Molecular structural descriptors. The molecular structural parameters of dyes were calculated by density function theory and 24 structural parameters and 2 physico-chemical parameters were used for QSPR model development. No. Descriptors Descriptions 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

EHOMO ELUMO η χ S Ω αxx αyy αzz α μ Qxx Qyy Qzz Qii q+ H

17

q−

18

qN

19 20 21 22 23 24 25 26 27

Eth C0v So ET Ho Go λmax Mw Y

Energy of the highest occupied molecular orbital (eV) Energy of the lowest unoccupied molecular orbital (eV) Absolute hardness (atomic units) Electronegativity (atomic units) Softness (atomic units) Electrophilicity index (atomic units) Molecular polarizability in the X coordinate (atomic units) Molecular polarizability in the Y coordinate (atomic units) Molecular polarizabilityin the Z coordinate (atomic units) Average molecular polarizability (atomic units) Total dipole moment (Debye) Quadrupole moment tensors in the X coordinate (Debye Å) Quadrupole moment tensors in the Y coordinate (Debye Å) Quadrupole moment tensors the Z coordinate (Debye Å) The average traceless quadrupole (Debye Å) The most positive net atomic charge on a hydrogen atom (atomic charge unit) The most negative atomic net charge of the molecule (atomic charge unit) The atomic net charge on the nitrogen atom (atomic charge unit) Internal energy (kcal/mol) Constant-volume heat capacity (cal/(mol k)) Entropy (cal/(mol k)) Total energy (Hartree) Enthalpy (Hartree) Free energy (Hartree) The maximum absorption wavelength (nm) Molecular weight (atomic mass units) Decoloration rate (%)

where C0 (mg/L) is the initial concentration of dyes and C (mg/L) is the residual concentration of dyes. All options of molecular structural descriptors were listed in Table 2 and the values of the all descriptors were listed in Table 3.

Therefore, it is important to eliminate redundant descriptors and identify important descriptors. This is the so-called variable selection. The following procedure was adopted [25]: Firstly, correlation analyses were performed between the discoloration rate (Y, %) value and all descriptors to find the most significant descriptor. A simple linear regression was established between the discoloration rate and the most significant descriptor. Secondly, one other descriptor was added and a PLS model was built and this step was repeated until every remaining descriptor had been added once and only once. The Q2(cum) value of all the models was compared and the PLS model with the biggest Q2(cum) value was selected into the step. If some models had the same Q2(cum) value, the descriptor with the highest VIP value was selected and entered into the model. Thirdly, the variable-addition and modelbuilding processes were repeated until all descriptors had been included. Finally, the optimal model from all models with the highest Q2(cum) value was selected and the number of PLS components for the optimal model was less than or equal to n/4.

2.5. Partial least squares (PLS) analysis

3. Results and discussion

In this study, PLS regression analysis was performed by using the Simca-P (Version 6.0, Umetri AB) which can analyze data with strongly collinear, noisy and numerous predictor variables [24]. Simca-P uses “cross validation” to determine the number of significant PLS components (A), and give a statistic Q2(cum) for the final PLS model. Q2(cum) denotes the cumulative variance of the dependent variable explained by the extracted PLS components and is a good measurement of the predictive power and robustness of the model. When Q2(cum) of a model is larger than 0.5, the statistical model can be believed to have a good internal predictive ability and robustness. In practice, internal predictive ability and robustness of the PLS model may decrease if irrelevant or insignificant relevant descriptors are included in QSPR analysis and it is difficult to explain the model.

PLS analyses were performed with 26 descriptors and discoloration rate of dyes as the dependent variable. All the models are listed in Table 4. In Table 4, A is the number of PLS components. R2X(adj)(cum) and R2Y(adj)(cum) stand for cumulative variance of all the X's and Y's explained by all extracted components. For example, from model (2), it can be concluded that two components were selected, which explained 0.733 and 0.979 of the variance of the independent and the dependent variables. R is the correlation coefficient between predicted and observed values. P is the significance level and RMSE is the root mean squared error. Q2(cum) is the cumulative cross-validated regression coefficient and the value of Q2(cum) is calculated to determine the goodness-to-prediction of the model, which indicates a model could be more robust if Q2(cum) value is larger than 0.5. In experience, a

Qii ¼

ðQxx þ Qyy þ QzzÞ 3

ð7Þ

where Qxx, Qyy, and Qzz are the values of quadrupole moment tensors in the x-,y-, and z-coordinates. The discoloration rate (Y, %) of dyes was expressed as: Y¼

ðC0‐CÞ  100% C0

ð8Þ

464

Y. Xu et al. / Journal of Molecular Liquids 215 (2016) 461–466

Table 3 Descriptors value for dyes. Density function theory was used to calculate the parameters' values of dyes and the results are listed in Table 3. Dyes

Cationic red GTL

Methyl orange

Acid red 8

Methyl red

Crystal violet

Indigo blue

Basic red 9

Rhodamine B

EHOMO ELUMO η χ S Ω αxx αyy αzz α μ Qxx Qyy Qzz Qii q+ H q− qN Eth C0v So ET Ho Go λmax Mw Y

−0.1846 −0.3028 0.2437 0.0591 2.0514 0.0072 555.885 265.848 125.774 315.8357 33.2054 −32.8205 −128.0338 −159.1427 −106.6657 0.221541 −0.429739 −0.622305 288.763 99.838 186.130 −1622.2737 −1622.2727 −1622.3612 530 390.89 99.5

−0.077 0.0070 0.0350 −0.0420 14.2857 0.0252 523.811 188.627 92.575 268.3377 30.8624 −285.8580 −132.0543 −145.2347 −187.7157 0.174218 −0.243005 −0.628225 179.180 73.226 151.210 −1329.282176 −1329.281231 −1329.3531 463 327.34 60.5

−0.0160 0.0750 −0.0295 −0.0455 −16.9492 −0.0351 590.042 286.322 124.987 333.7837 25.8073 −433.3928 −246.0844 −202.4709 −293.9827 0.224456 −0.481567 −0.312663 200.714 96.878 179.339 −2050.5124 −2050.5115 −2050.5967 505 480.42 20.5

−0.0710 −0.1910 0.1310 0.0600 3.8168 0.0137 427.850 177.862 89.654 231.7887 4.4106 −70.3290 −103.1041 −122.8339 −98.7557 0.381854 −0.558412 −0.627499 187.478 67.558 140.017 −894.7360 −894.7350 −894.8016 410 269.3 30.5

−0.287 −0.189 0.238 −0.049 2.1008 0.0050 559.057 559.137 158.993 425.729 0.0194 −64.6222 −64.6206 −162.9792 −97.4073 0.175250 −0.247133 −0.620906 334.949 103.983 182.939 −1133.9727 −1133.9718 −1134.0587 590 407.98 95.7

−0.197 −0.108 0.1525 −0.0445 3.2787 0.0065 433.308 190.288 52.793 225.463 0.0011 −84.3579 −114.8040 −117.4246 −105.5288 0.351439 −0.139798 0.828969 152.417 57.685 118.755 −875.2254 −875.2245 −875.2809 665 262.27 70.5

−0.309 −0.216 0.2625 −0.0465 1.9048 0.0041 470.372 470.282 59.402 333.352 0.0032 −28.9399 −28.9559 −136.8329 −64.9096 0.346756 −0.174183 −0.787272 219.735 68.478 123.681 −898.2695 −898.2686 −898.3273 550 323.82 98.2

−0.300 −0.197 0.2485 −0.0515 2.01207 0.0053 621.148 380.286 215.025 405.4863 4.6460 −72.6701 −148.6954 −185.8459 −135.7371 0.403628 −0.446818 −0.629734 360.955 118.204 200.679 −1419.7424 −1419.7108 −1419.8062 552 479.02 70.8

regression model with A components and number (N) training set compounds can be acceptable for N N 4A [26]. There were 8 dyes in the training set, so that the number of PLS components cannot be over 2, otherwise the model will appear over fitting. Associated with Q2(cum) and components of models (listed in Table 4), it can be found that model (2) is the desirable selection. In model (2), three descriptors

Table 4 Model fitting results from the variables selection. PLS analyses were performed with 26 descriptors, which obtained 25 QSPR models with variables selection. A is the number of PLS components; R2X(adj)(cum) and R2Y(adj)(cum) stand for cumulative variance of all the X's and Y's explained by all extracted components. Q2(cum) is the cumulative cross-validated regression coefficinet. R is the correlation coefficient between predicted and observed values. P is the significance level and RMSE is the root mean squared error. Models

A

Q2X(adj)(cum)

Q2Y(adj)(cum)

Q2(cum)

R

P

RMSE

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

1 2 3 4 4 4 4 4 3 3 3 3 3 3 4 4 4 4 4 4 4 4 1 1 1

0.607 0.733 0.938 0.957 0.96 0.965 0.964 0.866 0.831 0.789 0.779 0.799 0.815 0.827 0.894 0.901 0.863 0.868 0.874 0.874 0.863 0.849 0.362 0.347 0.328

0.839 0.979 0.995 0.998 0.999 0.999 0.996 0.998 0.988 0.991 0.991 0.992 0.992 0.989 0.996 0.996 0.998 0.998 0.998 0.995 0.993 0.994 0.674 0.696 0.71

0.777 0.845 0.962 0.993 0.992 0.976 0.936 0.84 0.621 0.612 0.705 0.76 0.776 0.766 0.791 0.781 0.754 0.714 0.674 0.605 0.667 0.668 0.242 0.254 0.239

0.9158 0.9893 0.9975 0.9992 0.9993 0.9993 0.9980 0.9991 0.9940 0.9953 0.9954 0.9958 0.9959 0.9944 0.9981 0.9978 0.9989 0.9989 0.9989 0.9976 0.9963 0.9967 0.8208 0.8343 0.8429

0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.013 0.01 0.009

13.1238 5.22809 2.83527 1.82335 1.71911 1.71119 2.90786 1.98302 4.38035 3.87835 3.8147 3.67258 3.61299 4.22697 2.83705 3.03147 2.15377 2.18856 2.13818 3.17288 3.94 3.71111 18.6602 18.0142 17.5806

η, μ and q− were included and hence it can be concluded that other descriptors were of less importance to the discoloration rate of dyes. From the PLS weights W*[1] and W*[2] (weights of variables) listed in Table 5, the significance of a single variable can be seen according to how it contributes in each PLS component to the modeling related to the descriptors. The absolute values of W*[1] for η and q− were 0.806657 and 0.561769 respectively, both of which were larger than the value of W*[1] for μ with − 0.183629. This demonstrates that η and q− are the main causes for the first component. The second PLS component is loaded on descriptor μ for which the W*[2] value was 0.906712 and larger than the values of W*[2] for η and q− with 0.522008 and 0.198236 respectively, therefore, μ contributes more in the second component. Variable importance in the projection (VIP), a parameter of PLS analysis, shows the importance of a variable in a model, which explains the relevance of predictor variables. If the VIP value of the descriptors is larger than 1, it is the most relevant for explaining dependent variables. The VIP values for the variables in model (2) are listed in Table 5, the VIP value of η was 1.2314, which indicates that it plays an important role in governing the discoloration rate of dyes by the Mo–Zn–Al–O catalyst. The pseudo-regression coefficients of the independent variables and constants transformed from PLS results are also listed in Table 5. From the coefficients of the independent variables, the effects of each independent variable on discoloration rate of dyes can be evaluated. Based

Table 5 The VIP values, PLS weights and pseudo-regression coefficientsa for model (2). The main descriptors governing discoloration rate of dyes were absolute hardness (η), dipole moment (μ) and the most negative atomic net charges of the molecule (q−). The VIP values, PLS weights and pseudo-regression coefficients of the descriptors are listed in Table 5. variables

VIP

W*[1]

W*[2]

Coefficients(a)

Coefficients(b)

η μ q− constants

1.2314 0.875415 0.846943

0.806657 −0.183629 0.561769

0.522008 0.906712 0.198236

0.914257 0.440888 0.532094 2.25731

252.644 0.899607 102.427 51.5042

a

Coefficients(a)—coefficients scaled and centered; Coefficients(b)—coefficients unscaled.

Y. Xu et al. / Journal of Molecular Liquids 215 (2016) 461–466

on unscaled pseudo-regression coefficients of the independent variables and constants from PLS, an analytical QSPR equation was developed as follows: Y ¼ 51:5042 þ 252:644η þ 0:899607μ þ 102:427q−

ð9Þ

n = 8, A = 2, R2X(adj)(cum) = 0.733, R2Y(adj)(cum) = 0.979, Q2(cum) = 0.845, R = 0.9893, P = 0.000, RMSE = 5.229. It can be seen from the equation that the two PLS components explained 0.733 of the variance of the independent variables and 0.979 of the variance of the dependent variable. Q2(cum) is a good measurement of the predictive power and robustness of the model. When Q2(cum) of a model is larger than 0.5, the statistical model can be believed to have a good internal predictive ability and robustness. The cross validated Q2(cum) value of the model is 0.845, demonstrating that the model has sufficient predictive ability and robustness. The model is able to be used to make predictions for the degradation of dye wastewater by Mo–Zn–Al–O catalyst. For the eight dyes contained in the training set, the value of R with 0.9893 indicates the significant relation between the predicted and observed values (Fig. 1). In Eq. (9), regression coefficients of η, μ and q− are positive, which shows that η, μ and q− play an active role in discoloration rate of dyes by Mo–Zn– Al–O catalyst. According to Eqs. (1) and (2), absolute hardness (η) and total dipole moment (μ) were defined as (ELUMO − EHOMO) / 2 and (ELUMO + EHOMO) / 2 respectively. The larger ELUMO possesses stronger electron-acceptance ability of the molecule; however, the smaller EHOMO possesses stronger electron-donor ability of the molecule. From Table 3, the values of η for cationic red GTL and acid red 8 were 0.2437 and −0.0295 respectively, and the discoloration rate for cationic red GTL and acid red 8 were 99.5% and 20.5% respectively. In the previous study, the Mo(IV)ZnAlO7H5 (Mo–Zn–Al–O) catalyst reacted with oxygen and water to form Mo(VI)Zn2AlO9H5 intermediate and H2O2 in the initial stage, then the catalyst can react with O2 by adsorption or activation of adsorbed O2 leading to the transfer of electrons between the metal atoms and O2 [10]. Oxygen is typically reduced to H2O2 and Mo(IV) is correspondingly oxidized to Mo(VI). A free radical chain auto oxidation process was performed to generate ·OH radical. Finally, this active oxygen radical reacted with dye and degraded the dye for other small molecules. Dyes which have bigger η easily combine with ·OH and have a higher discoloration rate by Mo–Zn–Al–O catalyst. The enormous difference in the value of η between cationic red GTL and acid red 8 resulted in the large difference in discoloration rate, which can be explained that η is a significant factor in governing the discoloration rate of dyes by Mo–Zn–Al–O catalyst. Zeta potential of the Mo–Zn– Al–O catalyst is negative and q− is the most negative atomic net charge

Fig. 1. Plot of observed and predicted discoloration rate value by model (2). The relationship between the observed and predicted discoloration rate of 8 dyes is listed in Fig. 1.

465

of the molecule therefore there is a repulsive force between the catalyst and the most negative atom [27]. From Table 3, the values of q− for Methyl red and basic red 9 were − 0.558412 and − 0.174183 respectively and the corresponding discoloration rate 9 was 30.5% and 98.2% respectively. The enormous difference value of q− between Methyl red and basic red 9 resulted in the large difference in discoloration rate. This means that q− is a great factor which impacts the discoloration rate of dyes. 4. Conclusions The present study was performed to apply the DFT-based quantum chemical descriptors in QSPR analysis in order to study the Mo–Zn– Al–O catalyst degradation of dyes. The DFT-based quantum chemical descriptors were obtained at the B3LYP/6-31G(d,p) level. The optimal QSPR model with Q2(cum) value of 0.845 and R value of 0.9893 explained 0.733 of the variance of the independent variables, and 0.979 of the variance of the dependent variable, indicating good goodness-of-fit, robustness and predictability. The absolute hardness (η), dipole moment (μ) and the most negative atomic net charges of the molecule (q−) are main descriptors in governing the discoloration rate of dyes by Mo–Zn–Al–O catalyst. The obtained QSPR model could be used for predicting the discoloration rate of dyes, and further studies would be needed to test additional dyes for external validation. Acknowledgment We gratefully acknowledge the financial support from the Research Foundation of National Natural Science Foundation of China (No. 51308484), Research Foundation of Natural Science Foundation of Hunan Province (No. 13JJ4049), Education Department Fund of Hunan Province (No.14C1094) and Specialized Research Fund for the Doctoral Program of Xiangtan University (No.12QDZ18). References [1] A. Pandey, P. Singh, L. Iyengar, Bacterial decolorization and degradation of azo dyes, Int. Biodeterior. Biodegrad. 59 (2007) 73–84. [2] T.G. Chuah, A. Jumasiah, I. Azni, S. Katayon, S.Y. Thomas Choong, Rice husk as a potentially low-cost biosorbent for heavy metal and dye removal: an overview, Desalination 175 (2005) 305–316. [3] S. Ozdemir, K. Cirik, D. Akman, E. Sahinkaya, O. Cinar, Treatment of azo dyecontaining synthetic textile dye effluent using sulfidogenic anaerobic baffled reactor, Bioresour. Technol. 146 (2013) 135–143. [4] G. Mishra, M. Tripathi, A critical review of the treatments for decolourization of textile effluent, Colourage 40 (1993) 35–38. [5] M.F. Elkady, A.M. Ibrahim, M.M. Abd El-Latif, Assessment of the adsorption kinetics, equilibrium and thermodynamic for the potential removal of reactive red dye using eggshell biocomposite beads, Desalination 278 (2011) 412–423. [6] W. Zhang, H.J. Li, X.W. Kan, L. Dong, H. Yan, Z.W. Jiang, H. Yang, Adsorption of anionic dyes from aqueous solutions using chemically modified straw, Bioresour. Technol. 117 (2012) 40–47. [7] J.H. Weisburger, Comments on the history and importance of aromatic and heterocyclic amines in public health, Mutat. Res. 506–507 (2002) 9–20. [8] A. Katsoni, H.T. Gomes, L.M. Pastrana-Martínez, J.L. Faria, J.L. Figueiredo, D. Mantzavinos, A.M.T. Silva, Degradation of trinitrophenol by sequential catalytic wet air oxidation and solar TiO2 photocatalysis, Chem. Eng. J. 172 (2011) 634–640. [9] H.Z. Wei, X.M. Yan, X.R. Li, S. He, C.L. Sun, The degradation of isophorone by catalytic wet air oxidation on Ru/TiZrO4, J. Hazard. Mater. 244-245 (2013) 478–488. [10] Y. Xu, X.Y. Li, X. Cheng, D.Z. Sun, X.Y. Wang, Degradation of cationic red GTL by catalytic wet air oxidation over Mo–Zn–Al–O catalyst under room temperature and atmospheric pressure, Environ. Sci. Technol. 46 (2012) 2856–2863. [11] Y. Li, Y. Xu, X.Y. Chen, F. Ge, R.L. Zhu, High catalytic activity of Mo–Zn–Al–O catalyst for dye degradation: effect of pH in the impregnation process, Appl. Catal. B Environ. 160–161 (2014) 115–121. [12] X.H. Zhu, G.H. Ding, W. Levy, G. Jakobi, K.W. Schramm, Relationship of air sampling rates of semipermeable membrane devices with the properties of organochlorine pesticides, J. Environ. Sci. 23 (supplement) (2011) S40–S44. [13] A. Sosnowska, M. Barycki, K. Jagiello, M. Haranczyk, A. Gajewicz, T. Kawai, N.Y. Suzuki, T. Puzyn, Predicting enthalpy of vaporization for persistent organic pollutants with quantitative structure–property relationship (QSPR) incorporating the influence of temperature on volatility, Atmos. Environ. 87 (2014) 10–18. [14] P. Lind, C. Lopes, K. Öberg, B. Eliasson, A QSPR study on optical limiting of organic compounds, Chem. Phys. Lett. 387 (2004) 238–242.

466

Y. Xu et al. / Journal of Molecular Liquids 215 (2016) 461–466

[15] G.G. Yu, L. Wen, D.C. Zhao, C. Asumana, X.C. Chen, QSPR study on the viscosity of bis(trifluoromethylsulfonyl)imide-based ionic liquids, J. Mol. Liq. 184 (2013) 51–59. [16] S. Timofei, W. Schmidt, L. Kurunczi, Z. Simon, A review of QSAR for dye affinity for cellulose fibres, Dyes Pigments 47 (2000) 5–16. [17] M. Rita Giorgi, R. Carpignano, A. Cerniani, Structure optimization in a series of thiadiazole disperse dyes using a chemometric approach, Dyes Pigments 37 (1998) 187–196. [18] X. Xu, F. Luan, H.T. Liu, J.B. Cheng, X.Y. Zhang, Prediction of the maximum absorption wavelength of azobenzene dyes by QSPR tools, Spectrochim. Acta A 83 (2011) 353–361. [19] R. Tabaraki, T. Khayamian, A.A. Ensafi, Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide, J. Mol. Graph. Model. 25 (2006) 46–54. [20] F.A. Pasha, M. Muddassar, H.W. Chung, S.J. Cho, H. Cho, Hologram and 3Dquantitative structure toxicity relationship studies of azo dyes, J. Mol. Model. 14 (2008) 293–302. [21] M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, J.A. Montgomery, T. Vreven, K.N. Kudin, J.C. Burant, J.M. Millam, S.S. Iyengar, J. Tomasi, V. Barone, B. Mennucci, M. Cossi, G. Scalmani, N. Rega, G.A. Petersson, H. Nakatsuji, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, M. Klene, X. Li, J.E. Knox, H.P. Hratchian, J.B. Cross, C. Adamo, J. Jaramillo, R. Gomperts, R.E. Stratmann, O. Yazyev, A.J. Austin, R. Cammi, C. Pomelli, J.W. Ochterski, P.Y. Ayala, K. Morokuma, G.A. Voth, P.

[22] [23] [24] [25]

[26] [27]

Salvador, J.J. Dannenberg, V.G. Zakrzewski, S. Dapprich, A.D. Daniels, M.C. Strain, O. Farkas, D.K. Malick, A.D. Rabuck, K. Raghavachari, J.B. Foresman, J.V. Ortiz, Q. Cui, A.G. Baboul, S. Clifford, J. Cioslowski, B.B. Stefanov, G. Liu, A. Liashenko, P. Piskorz, I. Komaromi, R.L. Martin, D.J. Fox, T. Keith, M.A. Al-Laham, C.Y. Peng, A. Nanayakka. ra, M. Challacombe, P.M.W. Gill, B. Johnson, W. Chen, M.W. Wong, C. Gonzalez, J.A. Pople, Gaussian 03, Revision B.05, Gaussian Inc, Pittsburgh, PA, 2003. C. Lee, W. Yang, R.G. Parr, Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density, Phys. Rev. B 37 (1988) 785–789. R.G. Parr, W. Yang, Density-Functional Theory of Atoms and Molecules, Oxford University Press, 1989. S. Wold, M. Sjöström, L. Eriksson, PLS-regression: a basic tool of chemometrics, Chemometr. Intell. Lab. 58 (2001) 109–130. G.H. Ding, X. Li, F. Zhang, J.W. Chen, L.P. Huang, X.L. Qiao, Mechanism-based quantitative structure–activity relationships on toxicity of selected herbicides to Chlorella vulgaris and Raphidocelis subcapitata, Bull. Environ. Contam. Toxicol. 83 (2009) 520–524. Y. Xu, D.Z. Sun, Structure and catalytic activity of Mo–Zn–Al–O catalyst for degradation of cationic red GTL under room conditions, Chem. Eng. J. 183 (2012) 332–338. A. Tropsha, P. Gramatica, V.K. Gombar, The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci. 22 (2003) 69–77.