Environmental Technology
ISSN: 0959-3330 (Print) 1479-487X (Online) Journal homepage: http://www.tandfonline.com/loi/tent20
Preparation and characterization of Cu2O nanoparticles and its photocatalytic degradation of fluroxypyr Xiaojiao Yu, Song Kou, Jie Zhang, Xiyan Tang, Qian Yang & Binghua Yao To cite this article: Xiaojiao Yu, Song Kou, Jie Zhang, Xiyan Tang, Qian Yang & Binghua Yao (2017): Preparation and characterization of Cu2O nano-particles and its photocatalytic degradation of fluroxypyr, Environmental Technology, DOI: 10.1080/09593330.2017.1370023 To link to this article: http://dx.doi.org/10.1080/09593330.2017.1370023
Accepted author version posted online: 21 Aug 2017.
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Date: 23 August 2017, At: 22:22
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Publisher: Taylor & Francis & Informa UK Limited, trading as Taylor & Francis Group
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Journal: Environmental Technology
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DOI: 10.1080/09593330.2017.1370023
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Preparation and characterization of Cu2O nano-particles and its
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photocatalytic degradation of fluroxypyr
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Xiaojiao Yu a,*, Song Koua, Jie Zhanga, Xiyan Tanga, Qian Yanga, Binghua
8
Yaoa
9 10 11
a
School of Science, Xi’an University of Technology, Xi’an 710048, China
*Correspondence: [email protected]; Tel.: +86 29 82066360
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Preparation and characterization of Cu2O nano-particles and its
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photocatalytic degradation of fluroxypyr
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Cu2O nano-particles were prepared by liquid-phase reduction method using
15
copper sulfate as raw material and the effect of the dispersant was studied. The
16
microstructure, surface morphology and optical properties of Cu2O nano-
17
particles were characterized by XRD, XPS, nitrogen static adsorption, SEM,
18
particle size analysis, UV-Vis and PL. The photocatalytic degradation of
19
fluroxypyr using Cu2O was studied by response surface methodology, and
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quadratic multinomial mathematical model was established. The results
21
indicated
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polyvinylpyrrolidone were of high purity with the preferential orientation of
23
(111). The particle size distribution range was about 400-800 nm, the average
24
particle size was 605.4±124.8 nm, the specific surface area was 22.641 m2/g, the
25
band gap was approximately 2.04 eV and the absorption edge was about 650
26
nm. R2 of established quadratic model was 0.9973 has a good fitness, indicating
27
established model was reliable. The optimal degradation conditions were
28
obtained as follows: the initial concentration of fluroxypyr was 11.17 mg/L, the
29
pH of the solution was 12.0, H2O2 concentration was 15 mg/L. Under the
30
optimum conditions, the degradation rate of fluroxypyr could reach 83.2% and
31
the relative error was 1.20%. After 9 times of recycling, more than 75% of
32
fluroxypyr can be degraded by Cu2O nano-particles.
33
Keywords: liquid-phase reduction; Cu2O nano-particles; response surface
34
methodology; photocatalytic; degradation fluroxypyr
that
prepared
Cu2O
crystal
particle
using
dispersant
of
35
1. Introduction
36 37 38 39 40 41 42 43 44 45
Wastewater emissions is increasing day by day with the continuous development of the society, especially the discharge of industrial wastewater which has the many pollutants of the organic matter with the high concentration, the bad biodegradability and even biological toxicity. Governance of them has always been a research hotspot. The treatment of organic pollutants in water by conventional water treatment technology is difficult to meet the needs of wastewater treatment due to the more byproducts, the complicated post-treatment and the secondary pollution. So scientists around the world are working to develop new technologies and to seek a new method of sustainable and efficient, economic, green degradation of organic wastewater [1, 2]. As a newly developed technology, photocatalytic technology can effectively remove toxic organic pollutants in the water and save energy by
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utilizing solar energy. It has great development prospects owing to its strong oxidizing ability, no secondary pollution and mild operating conditions [3-6], in which the most compelling of semiconductor photocatalytic oxidation with the outstanding advantages, all kinds of pollutants degraded completely in wastewater, the reaction thoroughly, cheap raw materials, chemical stability and can make use of the sunlight. At present, there are many researches on semiconductor photocatalyst, such as TiO2, ZnO, CuO, and Cu2O and so on [7-10]. These photocatalysts can decompose a large number of toxic and refractory pollutants into CO2, H2O and corresponding inorganic ions. Nevertheless, major problem of photocatalysts are low efficiency of solar energy utilization and recombination of photogenerated electron-hole pairs. For this reason, many researchers have studied the modification of semiconductor photocatalysts or sought new semiconductor photocatalysts [11-15]. The improvement of photocatalytic efficiency of photocatalyst is also a research hotspot.
58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
Cu2O is a kind of direct band gap semiconductor with a band gap of 2.0 eV which has high optical absorption coefficient and can be used for hydrogen production from photocatalytic water, photocatalysis, sensor, lithium ion battery and solar cell [16-20]. At present, the use of Cu2O in the degradation of pollutants has been carried out [21-24]. Mishra et al. [25] showed that the growth rate of (111) plane increased with the increase of the precursor concentration and temperature, and the (100) plane of the cubic Cu2O crystal has a higher stability than (111) plane after repeated 20 times with the treatment of industrial wastewater containing Cr (VI). Wang et al. [26] studied shows that the layered structure of petal-like Cu2O crystal not only increases the specific surface area and pore volume, but also improves the efficiency of lightharvesting, effectively promotes the separation efficiency of electron and hole. Sabbaghanet et al. [27] obtained the octahedral, nanorod and nanoparticle structures with sizes ranging from 18-32 nm by changing the type of reductant using a refluxing method, proposing a possible mechanism to explain the formation of different morphologies of the Cu2O nanostructures. Some studies [28, 29] showed that Cu2O prepared by the liquid-phase reduction method has good dispersibility, stability and controllable morphology. Cu2O as a photocatalyst was widely studied in the area of the degradation of organic pollutants with the great advantage in the field of visible light catalytic. However, its dispersion, stability and morphology control remains to be studied in depth.
76 77 78 79 80 81
Response surface methodology (RSM) is an efficient and simple statistical technique based on mathematics and statistics. It was used to establish a model and evaluate the influence factors as well as their interactions, thereby to determine the optimal horizontal range and obtain the desired reaction conditions [30]. This method can effectively predict the specific process conditions of the target response and reduce the number of experiments and time. So RSM has been widely used in the treatment of wastewater [31-36].
82 83 84 85 86
This process involved Cu2O nano-particles were prepared using liquid phase reduction method with glucose as reducing agent and discussed the effect of dispersant on the properties of Cu2O nano-particles. The prepared Cu2O nano-particles were used to catalyze degradation fluroxypyr, which was studied by reaction surface method and the experimental results were obtained in the presence of catalyst.
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2. Materials and Methods
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2.1. Preparation of Cu2O nano-particles
89 90 91 92 93 94 95 96 97 98 99 100 101
Copper sulfate (CuSO4, Guangdong Guanghua Technology Co., Ltd. China), sodium hydroxide (NaOH, Tianjin Kemiou Chemical Reagent Co., Ltd. China), glucose (C6H12O6, Tianjin Shengao Chemical Reagent Co., Ltd. China) and polyvinylpyrrolidone K30 (PVP K30, Chengdu Kelong Chemical Reagent Factory, China) were used for Cu2O nano-particles preparation by liquidphase reduction method [37, 38], all of the above chemicals are of analytical grade and use as received without further purification. The solution of 10 mL, 0.5 mol/L CuSO4 and 20 mL, 1 mol/L NaOH was mixed to a breaker as solution (1), and then ultrasonic oscillation was performed until precipitate completely. The 17 mL, 0.65 mol/L glucose was added to solution (1) as solution (2), and solution (2) was stirred until which became orange-yellow. Subsequently, solution (2) was filtered using a sand core funnel and washed 3 times with distilled water and ethanol successively, drying at 45 °C to be powdered products, denoted as S0. The additive PVP K30 was added to solution (1). Then the above experiment process was repeated to obtain a sample S1.
102
2.2. Structural characterization of Cu2O nano-particles
103 104 105 106 107 108 109 110 111
A 6100 type X-ray diffractometer (XRD, Shimadzu, Japan, Cu Kα, λ=1.54056 Å), a K-Alpha Xray photoelectron spectroscopy (XPS, Thermo Fisher Scientific, America, Al Kα, 150 W, 15 KV, 1486.71 eV) and a JW-BK122W static nitrogen adsorption (BET, Beijing JingweiGaobo, China) were employed for the analysis of the microstructure of Cu2O nano-particles. A TESCAN VEGA3 scanning electron microscope (SEM, TESCAN, Czech Republic) and a DelsaNano C particle analyzer (Beckman Coulter, USA) were utilized to characterize the surface morphology and structural characteristics of the samples. A UV-3600 ultraviolet visible light spectrophotometer (UV-Vis, Shimadzu, Japan) and a Fluoromax-4 PL spectrometer (HORIBA JobinYvon, France) were used to characterize optical properties of Cu2O nano-particles.
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2.3. Degradation fluroxypyr
113 114 115 116 117 118 119 120 121
The photocatalytic degradation of fluroxypyr by Cu2O nano-particles was carried out in a photochemical reaction apparatus [9]. Firstly, 0.1 g of Cu2O nano-particles were dispersed in 100 mL fluroxypyr solution with a certain concentration and the solution was stirred in the dark for 30 min to evaluate the adsorption capacity. Then, a certain volume of H2O2 was added to the suspension of Cu2O, and pH was adjusted to 12 with 1 mol/L NaOH solution. The photocatalytic reaction was carried out with 500 W metal halide lamp as the light source (the light of below 400 nm was filtered by filter and the main wavelength was 400-1100nm) at 45 ℃, taking a sample every 30 min and using spectrophotometric determination of fluroxypyr concentration.
122
2.4. Experimental design for response surface methodology
123 124 125 126
The influence of various factors on degradation rate of fluroxypyr was considered synthetically on the basis of single factor experiment. According to the center combination test of Box-Behnken and Design Expert, the degradation condition was optimized and the experimental result was regression fitted with quadratic polynomial to predict the best
127 128 129 130
conditions of degradation. The degradation rate of fluroxypyr by Cu2O nano-particles was considered as the response (Y). The pH (A), dosage of H2O2 (B) and initial concentration of fluroxypyr (C) were used as independent variables. The symbols and coding levels were shown in table 1, and design and experimental results were shown in table 2.
131 132 133
Table 1. Symbols and coded levels of the independent variables Independent variable (symbols) pH A c H2O2/(mg/L) B c/(mg/L) C
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Factors
134
-1 5.0 1.0 5.0
Code levels 0 8.5 8.0 27.5
1 12.0 15.0 50.0
Table 2. Design and experimental results of response surface method
Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Coded values of independent variables A B C 0 -1 -1 0 0 0 0 0 0 1 0 -1 1 -1 0 -1 1 0 0 0 0 1 0 1 0 1 1 0 0 0 -1 0 1 0 -1 1 0 0 0 -1 0 -1 -1 -1 0 0 1 -1 1 1 0
Degradation rate (%) 37.33 47.79 47.79 78.07 70.94 50.00 47.79 33.57 17.82 47.79 19.92 13.34 47.79 0.776 36.06 53.13 67.60
135 136
3. Results and Discussion
137
3.1. The characterization of XRD
138 139
Figure 1 shows the XRD pattern of the prepared Cu2O nano-particles. It can be seen from figure 1, the diffraction peaks of S0 and S1 coincided with the standard card (JCPDS 05-0667).
No other diffraction peaks appeared in the XRD spectrum, indicating that both S1 and S0 have high purity. A strong and sharp diffraction peak appeared at 2θ of 36.42 °, corresponding to the (111) crystal face of Cu2O, showing that the prepared Cu2O crystal by this method was more likely to grow on the (111) crystal plane. Both S1 and S0 have had high purity with the preferential orientation of (111) crystal plane strong. Compared to the sample S0, the (111) crystal face of the sample S1 shows stronger diffraction intensity in the XRD pattern. The change in XRD intensity of faceted crystals can be attributed to different growth rates along different crystal planes of the Cu2O crystal. The crystal face with high surface energy grows at a faster rate in vertical direction. The crystal face with low surface energy grows at a slower rate in vertical direction. Whereby the high surface energy crystal faces decrease and the area of the low surface energy crystal face is increased [39, 40]. Due to the relative order of the surface energy of Cu2O each crystal is (100) > (110) > (111). Therefore, the prepared Cu2O in most cases has (111) preferential orientation. The reported [41] that the PVP preferential adsorption was taken at (111) crystal face of Cu 2O crystal made it possible to delicately control the volume fraction ratio of (100) to (111). This makes the growth rate perpendicular to the (111) crystal face to be slower. So that the (111) crystal plane areas increased, and thus sample S1 showed stronger diffraction intensity in the XRD pattern [25]. The high catalytic activity of (111) crystal plane has been proved [42]. (111)
(200)
Intensity(a.u.)
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140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157
(110)
(220) S1
(311)
S0
JCPDS 05-0667
20
158 159
30
40
50
2
60
70
80
Figure 1. XRD patterns of prepared Cu2O nano-particles from different method
160
3.2. The characterization of XPS
161 162 163 164 165 166
Figure 2 shows the XPS pattern of the prepared Cu2O nano-particles. It can be seen from high resolution XPS pattern, figure 2(a) and figure 2(b) were three peaks at 932.3, 952.1 and 529.7 eV, corresponding to Cu 2p3/2, Cu 2p1/2, and O 1s binding energy of Cu2O, respectively. And no impurity peaks appeared between Cu 2p3/2 and Cu 2pl/2, revealing that no Cu or CuO exists in the Cu2O nano-particles. The results showed that the Cu2O nano-particles of highly purity were prepared by this method and it is consistent with the results of XRD analysis.
(b)
(a) 932.3eV
O1s
529.7eV
952.1eV Cu 2p1/2
925
930
935
945
950
955
960
965
524
526
528
530
532
534
536
538
Binding energy/eV
Figure 2. (a): Cu 2p XPS spectra; (b): O 1s XPS spectra of Cu2O nano-particles 3.3. The Characterization of BET
170 171 172 173 174 175 176 177 178 179 180 181 182 183
Figure 3 shows the N2 adsorption isotherm curve and pore size distribution of the Cu2O nano-particles. It can be seen from figure 3 that the N2 adsorption-desorption isotherms of the Cu2O nano-particles were in good agreement with the isotherm IV, and the Cu2O nanoparticles were adsorbed in the low pressure region with a relatively moderate increase, indicating N2 molecules were single layer to multi-layer adsorption of Cu2O nano-particles on the surface. In the range of P/P0=0.5-1.0, adsorption capacity was the emergence of a sudden increase, due to the stage was N2 rapidly condensation accumulation process on the Cu2O nano-particles surface. The BET specific surface area and average pore diameter of S0 were 8.024 m2/g and 7.401 nm, respectively, and the BET specific surface area and average pore diameter of S1 were 22.641 m2/g and 7.694 nm, respectively. This indicated that S0 and S1 were both mesoporous structure and S1 has had a larger specific surface area than S0, which can provide more surface active sites in the catalytic degradation reaction and transfer the carrier charge easily. It is beneficial to the reaction. It is further explained the dispersibility of Cu2O nano-particles can be improved by the addition of surfactants.
185
0.000
0
5
10
15
20 25 Pore diameter(nm)
30
10
0 0.0
184
0.005
0.2
0.4
0.6
0.8
Relative pressure(P/P0)
1.0
40 30
Pore volume (cc/g,nm )
20
50 S0
0.010
Adsorbed volume(cc/g,STP)
30
Pore volume(cc/g,nm)
169
Adsorbed volume(cc/g,STP)
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940
Binding energy(eV)
167 168
Intensity(a.u.)
Intensity(a.u.)
Cu 2p3/2
S1
0.015 0.010 0.005 0.000
0
5
10
15
20
25
30
Pore diameter(nm)
20 10 0 0.0
0.2
0.4
0.6
0.8
1.0
Relative pressure(P/P0)
Figure 3. Nitrogen absorption isotherm curve and pore size distribution setting under 77 K.
3.4. SEM and Particle size distribution analysis
187 188 189 190 191 192 193 194 195 196 197 198 199
The SEM and particle size distribution of Cu2O nano-particles were shown in figure 4. Figure 4 show that the particle size of Cu2O nano-particles was particle size of Cu2O nano-particles with polyvinylpyrrolidone K30 was basically the same as that of Cu2O nano-particles with no surfactant. However, the dispersibility of the sample S1 was greatly improved and the agglomeration phenomenon was obviously reduced compared to the sample S0. This may be due to the non-ionic surfactant adsorbed on the surface of Cu2O nano-particles, forming a steric repulsion, so that Cu2O nano-particles more stable and dispersed [43]. The particle size distribution shows that the particle size of S0 distribution range was about 500-900 nm, the average particle size was 726.4±136.5 nm. The particle of S1 size distribution range was about 400-800 nm, the average particle size was 605.4±124.8 nm, which were basically consistent with the results of SEM. Moreover, the strength of the S0 sample at about 10 4 nm was significantly higher than that of the S1 sample, which also shows that the adding PVP K30 improved the dispersibility and reduced the agglomeration of the Cu2O nano-particles.
Differential Intensity(%)
12
S0
10
D=726.4±136.5nm
8 6 4 2 0 10
2
10
3
10
4
Diameter(nm)
200 12
Differential Intensity(%)
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186
S1
10
D=605.4±124.8nm
8 6 4 2 0 10
10
3
10
4
Diameter(nm)
201 202
2
Figure 4. SEM image and particle size distribution curve of Cu2O nano- particles
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3.5. UV-Vis analysis of Cu2O nano-particles
204 205 206
Figure 5 shows UV-Vis absorption spectra of Cu2O nano-particles prepared. It can be seen from figure 5(a) that the absorption boundary of Cu2O nano-particles with PVP K30 was about 650 nm, and the absorption of light in the range of 400-650 nm was relatively large, reaching
207 208 209 210 211 212
the maximum at 516 nm. Compared to the Cu2O nano-particles without the PVP K30, the absorbance of the sample S1 has been greatly improved. As shown in figure 5(b), it can be estimated that the forbidden band width of S0 was 1.98 eV and S1 was 2.04 eV, according to the previous literature [44, 45]. These results indicated that the prepared Cu2O nano-particles have had a good absorption in the visible range, and the absorption intensity of Cu 2O nanoparticles added PVP K30 was obviously increased, and the band gap was slightly increased. 1.0
(a)
S1
(b)
S1
1.5
S0
(h)2
Absorbance
0.8 0.6 0.4
S0
1.0
0.0 400
213 214
500
600
700
0.0 1.6
800
1.8
2.0
2.2
2.4
2.6
2.8
heV
Wavenumber(nm)
Figure 5. UV-Vis adsorption spectra of Cu2O nano- particles
215
3.6. PL analysis of Cu2O nano-particles
216 217 218 219 220 221 222 223 224 225
Figure 6 shows the photoluminescence spectrum of Cu2O nano-particles. It can be seen from figure 6, the shape and position of the PL spectra of the sample S1 and S0 were basically the same, the photoluminescence intensity of prepared Cu2O nano-particles adding PVP K30 was higher than that without addictive agent. This illustrates that adding the PVP K30 only affected the response range and intensity of PL spectra of Cu2O nano-particles. The photoluminescence intensity was related to the separation efficiency of the electron-hole pairs. The higher the photoluminescence intensity, the higher the separation efficiency of the electron-hole pairs, and the more holes that can produce hydroxyl radicals with the high of catalytic activity [46]. According to this conclusion, we can conclude that prepared Cu2O nano-particles adding PVP K30 may well shows higher catalytic activity. S1
Intensity(a.u.)
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0.5 0.2
S0
470
227 228
480
490
500
510
520
530
Wavenumber(nm)
226
Figure 6. The photoluminescence spectrum of Cu2O nano-particles (the excitation wavelength is 383 nm)
229
3.7. Analysis of response surface design of the degradation of fluroxypyr
230 231 232 233
The degradation experimental data of fluroxypyr by Cu2O nano-particles in table 2 was analyzed through quadratic regression fitting using software Design-Expert 8.0 software and quadratic multinomial regression equation (1) were obtained to study the effects of the experimental variables on the photodegradation of fluroxypyr:
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234
Y%=47.79+17.93A+3.86B-10.58C-4.32AB-15.91AC-2.83BC+5.52A2+2.83B2-20.23C2
(1)
235 236 237 238 239 240 241 242 243 244 245 246
The model F-value was the ratio between of mean square between groups in the ANOVA analysis, which can reflect the importance of each factor for the degradation rate of fluroxypyr, used for evaluation significant of the fitted model [35]. Table 3 implies that the established quadratic polynomial model was highly significant (p initial concentration of fluroxypyr (FC=18.41) > dosage of H2O2 (FB=2.45). When the initial concentration of fluroxypyr was 11.17 mg/L and the dosage of H2O2 was 15 mg/L, the degradation rate of fluroxypyr was higher. Meanwhile, table 3 also indicates that quadratic term C2 was significant. The interaction terms AC (“Prob>F” ＜ 0.05) expressed the significant model, and AB and BC of interaction terms shown insignificance. It is known that the 'Prob> F' value is less than 0.05, which means that the index is significant.
247 248 249 250 251 252 253 254 255
On the other hand, to determine the goodness of fit between the model and experimental data, the correlation coefficient is often used [47]. The regression model determines the coefficient R2=0.9504, which indicated that the model was well fitted. Experimental point distribution evenly closed to a straight line, which implied the response value of 95.04% can be explained. The regression model variance was analyzed. Figure 7(a) shows that standard residual were basically consistent with normal distribution satisfactorily, and the error of this model was mainly systematic error within the controllable category. Figure 7(b) displays that the predictive value of the model was a good match with true values. Therefore, the model can be used to analyze and predict the degradation rate of fluroxypyr.
256
Table 3. Regression model and variance analysis Source
Sum of squares
Degree of freedom
Mean square
F-value
Prob> F
Model
6529.20
9
725.47
14.91
< 0.0009
A
2571.31
1
2571.31
52.83
< 0.0002
B
119.20
1
119.20
2.45
0.1616
C
895.83
1
895.83
18.41
< 0.0036
AB
74.65
1
74.65
1.53
0.2555
AC
1012.64
1
1012.64
20.81
< 0.0026
BC
32.04
1
32.04
0.66
0.4439
A2
128.27
1
128.27
2.64
< 0.1485
B2
33.97
1
33.97
0.70
< 0.4310
C2
1722.40
1
1722.40
35.39
0.0006
Residual
340.67
7
48.67
Lack of fit
340.67
3
113.56
Pure error
0
4
0
Correlation total
6869.87
16
R2=0.9504
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257 258 259 260 261
The perturbation plot of degradation rate for fluroxypyr was shown in figure 8. In figure 8, the curve of initial concentration of fluroxypyr (independent variable C) has had the biggest change and the highest sensitivity, indicating that was greatest influence for the degradation rate of fluroxypyr. This is consistent with the results of shown in Table 3.
262 263 264
Figure 7. Normal probability diagram of the residual (a) and predicted versus actual values plot (b) for degradation
265 266 267 268
Figure 8. Perturbation plot of degradation rate for fluroxypyr The impact of notable interactions terms AC was investigated on the degradation efficiency. Figure 9 shows the interaction effects of the initial concentration of fluroxypyr and pH under
the optimal dosage of H2O2 (15 mg/L). The results reveal that the degradation rate of fluroxypyr increased with the increase of pH. It can be also seen from figure 9, when the initial concentration of fluroxypyr was in the range of 14-41 mg/L, the degradation rate of fluroxypyr was higher. The optimum degradation conditions of fluroxypyr were obtained by the model: the initial concentration of fluroxypyr was 11.17 mg/L, the pH value of the solution was 12, the H2O2 concentration was 15 mg/L. Under the optimal conditions, the degradation rate of fluroxypyr was predicted to be 84.2%.
276 277
Figure 9. Isoheight (a) and response surface (b) for degradation rate as a function initial
278
concentration of fluroxypr and pH
279 280 281 282 283
According to the optimized conditions, the kinetic curves of the degradation of fluroxypyr by Cu2O nano-particles were obtained in figure 10, showing that the degradation rate of fluroxypyr was 83.2%, the error of which was only 1.20% compared with the expected degradation rate. Therefore, it is feasible to use the response surface method to optimize the degradation of fluroxypyr by Cu2O nano-particles, which has practical application value. 100 80
Degradation rate(%)
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269 270 271 272 273 274 275
60 40 20 0 0
284
60
120
180
Time(h)
285
Figure 10. Kinetic curve for degradation of fluroxypyr by Cu2O nano-particles
286 287 288 289 290
The reuse performance of Cu2O nano-particles for degradation fluroxypyr was shown in figure 11. It can be seen form figure 11, with the increase in the number of using Cu2O nanoparticles, the photocatalytic performance shown slowly decline. The photocatalytic degradation rate of fluroxypyr can reach more than 75% after the use of 9 times. It can be concluded that the prepared Cu2O nano-particles have relatively high reusability.
100
Degradation rate(%)
80
83.2 82.9 81.8 80.9 80.4 79.3 78.7 77.5 76.8
60 40 20 0 1
2
293 294 295 296 297 298 299
4
5
6
7
8
9
Figure 11. The influence of reuse for Cu2O nano-particles on photocatalytic activity Figure 12 shows XRD patterns of the nano-sized Cu2O after each cycle. As can be seen from figure 12, Compared to the primary sample, the peak of first degradation had a significant decline, and the intensity was reduced after each cycle. Figure 12 also shows that CuO was detected and no Cu was observed after repeat performed of photocatalytic process, indicating Cu2O was oxidized in the presence of hydrogen peroxide [48, 49]. This may be due to the oxidation reaction of Cu2O is more favorable kinetically than the reduction reaction [50], resulting the degradation rate of fluroxypyr was reduced after each recycling.
Intensity(a.u.)
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292
3
Usage Counter of Cu2O
291




9th cycle 8th cycle


7th cycle


6th cycle


5th cycle


4th cycle


3rd cycle


2nd cycle
CuO CuO 

1st cycle sample S1
20
300 301
30
40
50
2
60
70
80
Figure 12. XRD patterns of the Cu2O nano-particles after each cycle.
302
4. Conclusions
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In summary, Cu2O nano-particles were successfully prepared by liquid-phase reduction method, and its microstructure were characterized. Furthermore, the degradation of fluroxypyr using Cu2O nano-particles was studied by response surface design. Conclusions as follows:
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The optimum preparation conditions of Cu2O nano-particles were obtained by the discussion of experimental conditions. The prepared Cu2O nano-particles show higher purity, (111) crystal plane preferred orientation and better dispersity with low band gap. The Cu2O nano-particles displayed better absorption to the visible light. The regression model was established successfully using response surface design. The result indicated that established
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model was well fitted, and the model can be used to analyze and predict the degradation rate of fluroxypyr. The optimum degradation conditions were obtained. Under the optimal degradation conditions, the degradation rate of fluroxypyr was 83.2%.The degradation rate still was more than 75% after repeated 9 times for photocatalytic degradation of fluroxypyr. This concludes that the Cu2O nano-particles have potential application in photodegradation of organic wastewater under sunlight irradiation.
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Acknowledgments
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This work was supported by the National Science Foundation of China (Nos. 21576220,
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21276208), the S&T plan project of Shaan Xi Provincial Government (No. 2015JZ005).
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Disclosure statement
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The authors declare no conflict of interest.
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