J Solution Chem (2013) 42:27–43 DOI 10.1007/s10953-012-9952-y
Viscometric and Morphological Properties of Novel Magnesium Electrolyte–Polyacrylamide Composite Polymers in Aqueous Solution Khai Ern Lee • Imran Khan • Norhashimah Morad • Tjoon Tow Teng Beng Teik Poh
•
Received: 7 November 2011 / Accepted: 21 March 2012 / Published online: 15 January 2013 Ó Springer Science+Business Media New York 2013
Abstract The viscometric properties of novel magnesium electrolyte–polyacrylamide composite polymers in aqueous solutions were investigated using response surface methodology. Independent factors such as concentration of the magnesium electrolyte (magnesium chloride and magnesium hydroxide), concentration of polyacrylamide, and the solution temperature were taken into account for viscometric modeling. Experiments were carried out according to central composite design, which includes factorial, central and axial points of the factors. Solution viscosity was taken as the response variable. A polynomial model for the viscometric properties was developed using ANOVA and nonlinear regression analysis, and the R2 values are 0.9995 and 0.9996 for aqueous solutions of magnesium chloride–polyacrylamide (MCPAM) and magnesium hydroxide–polyacrylamide (MHPAM) composite polymers, respectively. Two diagnostic plots have been constructed to validate the developed models for the natural logarithm of viscosity of aqueous solutions of the MCPAM and MHPAM composite polymers. The least-squares values show that the developed models are adequate for predictive purposes. TEM was used to investigate the morphological properties of MCPAM and MHPAM composite polymers. Magnesium chloride was impregnated into the polyacrylamide chain while magnesium hydroxide was just adsorbed on the surface of the polyacrylamide chain. Keywords Viscosity Morphology Magnesium chloride Magnesium hydroxide Polyacrylamide Composite polymer
Electronic supplementary material The online version of this article (doi:10.1007/s10953-012-9952-y) contains supplementary material, which is available to authorized users. K. E. Lee I. Khan N. Morad (&) T. T. Teng B. T. Poh School of Industrial Technology, Universiti Sains Malaysia, 11800 Penang, Malaysia e-mail:
[email protected]
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1 Introduction The development of new composite polymers is a particularly active area, due to their superior performance compared to those of conventional inorganic and organic polymers in wastewater treatment [1]. Composite polymers refer to combinations of two different types of components, typically inorganic coagulating salts and organic water-soluble polymers in one material, to increase the aggregating capability of the polymers [2]. Addition of an effective component into the polymer matrix induces synergetic effects of the two components in removing pollutants from wastewater [3]. Physical blending of inorganic coagulating salts with organic water-soluble polymers, which do not contribute to any new chemical species, has been suggested as a method to prepare composite polymers [4]. There are a few publications that reported the development of inorganic–organic composite polymers, comprised of polydiallyldimethylammonium chloride (PDADMAC) with the inorganic salts ferric chloride [1], polyferric chloride [5, 6] and polyaluminium chloride [7]. Similarly, in our previous studies, the preparation of polyacrylamide based inorganic– organic composite polymers that contain calcium chloride [8], ferric chloride [9], magnesium chloride and magnesium hydroxide [10, 11] has been accomplished. However, very limited work has been reported on the investigation of viscometric properties of composite polymers in aqueous solution. Response Surface Methodology (RSM) is a collection of mathematical and statistical techniques that are used to investigate the effects of several factors at different levels, as well as their simultaneous interactions [12]. RSM was chosen for this study. It has been proven to be an effective technique to evaluate the effects of several factors and their interactions on the experimental responses with a limited number of planned experiments [13, 14]. Therefore, it is extensively used for scientific and technical application in applied chemistry and physics, biochemistry and biological engineering, chemical engineering and environmental protection [14–16]. Food industries in particular have been using the RSM technique to investigate the viscometric properties of foods under the influence of different factors [17–21]. However, the application of RSM in testing the solution viscosity of materials used in environmental applications has yet to be investigated. Acrylamide-based polymers are organic water-soluble polymers with high molecular weight, which have received diverse modifications and applications in the fields of wastewater treatment, water purification, mineral processing, friction reduction, and so forth [22–25]. Magnesium salts are used as effective alternatives to conventional coagulating agents [26–28]. In this study, aqueous solutions of novel composite polymers (MCPAM and MHPAM), which are composed of magnesium electrolytes (e.g. magnesium chloride and magnesium hydroxide) and polyacrylamide, were prepared. RSM experiments were designed using CCD to investigate the viscometric properties of aqueous composite polymer solutions. Three factors, i.e. the concentration of magnesium electrolyte, concentration of polyacrylamide and temperature of the solution were taken as the independent variables, while the solution viscosity was the response variable (dependent variable). The experimental results were analyzed using Design Expert 6 software. An empirical polynomial regression model was constructed based on the related, significant independent terms, and their interactions to describe the response surface. The fitness of the proposed model was determined using analysis of the variance (ANOVA). TEM was used to investigate the formation of the composite polymer in aqueous solution.
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2 Materials and Methods 2.1 Materials Ammonium persulfate (AR, Systerm) and sodium bisulfite (GR, Acros Organics) were used as redox initiators. Monomeric acrylamide (AM) ([99 % purity, Merck) was used without further purification. Magnesium chloride hexahydrate (A.R, Bendosen) and magnesium hydroxide (95–100 %, Systerm) were used as received. Deioinized water was obtained from a deionized water dispenser (Sartorius, Arium 611DI) and it was used for the redox polymerization. 2.2 Preparation of Aqueous Magnesium Salt–Polyacrylamide Composite Polymer Solutions Polyacrylamide was synthesized through redox polymerization in a single batch reactor that consisted of a three-necked reaction flask with a reflux condenser, thermostatic stirred water bath, and oxygen-free nitrogen gas inlet. A 1.0335 molL-1 acrylamide solution was transferred into the reaction flask and thoroughly stirred. Oxygen-free nitrogen was purged into the acrylamide solution for 10 min to deoxygenate the solution. The temperature of thermostatic stirring water bath was controlled at 65 °C before the redox polymerization took place. To initiate redox polymerization of polyacrylamide, 5 9 10-4 molL-1 ammonium persulfate and 1 9 10-4 molL-1 sodium bisulfate were injected into the acrylamide solution [24]. The redox polymerization was carried out for 10 min after the redox initiator couple was injected into the acrylamide solution and then terminated. The polymerized acrylamide solution was treated with cold acetone to precipitate the polyacrylamide. Polyacrylamide was separated by filtration and dried in an oven at 150 °C until it attained a constant weight. The average molecular weight of polyacrylamide produced is 1.66 9 106 Da with the degree of polymerization of 23,353 monomeric units. Different concentrations of aqueous magnesium electrolyte–polyacrylamide (MCPAM and MHPAM) composite polymer solutions were prepared through physical blending of polyacrylamide with magnesium chloride and magnesium hydroxide, respectively. The aqueous composite polymer solutions were allowed to age for 24 h at room temperature prior to any viscometric measurements. 2.3 Solution Viscosity Measurements The solution viscosity was determined through Eq. 1 using a Ubbelohde viscometer with calibration constant of 0.02491 mm2s-2 and the density using a density meter (Anton Paar, DMA 38). The uncertainty in density measurements was found to be ±0.0001 gcm-3. The viscosity of the aqueous magnesium salt–polyacrylamide composite polymer solutions was measured in a thermostatic water bath with a temperature tolerance of ±0.1 °C to control the temperature of the solution. The viscosity is given by: g ¼ A q t;
ð1Þ
where g is solution viscosity, A is calibration constant of the viscometer, q is density of the solution, and t is flow time. Degassed distilled water was used as the calibration fluid for the Ubblohde viscometer and density meter from temperatures of 15–40 °C (288–313 K). The measured density and
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viscosity were compared with the calibration data of density and viscosity of water [29]. Comparison of measured density and viscosity of water with literature data are shown in Fig. SM 1 and SM 2 of the supplementary material, respectively. Both density and viscosity show the high accuracy of measurements, with the coefficient of determination (R2) values of 0.9996 and 0.9977, respectively. 2.4 Response Surface Methodology (RSM) RSM was employed in simulating the viscometric properties of aqueous magnesium electrolyte–polyacrylamide composite polymer solutions. Central Composite Design (CCD) is one of the standard designs used in RSM. In CCD, factors were coded as xi where x1 represents the concentration of magnesium electrolyte, x2 represents the concentration of polyacrylamide, and x3 represents the temperature of the solution. As the factors are usually described in different units and different limits of variation, the significance of the factors on the response is compared after they are coded. The real values of the factors are shown in Table 1 where they were determined through the following equation: xi ¼
Xi x0 dx
ð2Þ
where xi is the coded value of the ith test variable, Xi is the corresponding uncoded value of the ith test variable, x0 is the value of xi at the center point of the investigated range and dx is the step change. Symbols –1 and ?1 represent the lowest level and highest level of the design, respectively, whereas symbol 0 represents the center point of the design. The CCD consists of a 23 factorial design with 4 center points, 6 augmented axial points coded ±a, and 2 axial center points (all factors set at the zero level) as shown in Table 2. The viscosity g of the aqueous composite polymer solution was used as the dependent response fitted by a second-order model in the form of a quadratic polynomial equation: g ¼ b0 þ
k X
bi xi þ
i¼1
k X
k¼1 X k X
bii x2i þ
i¼1
ð3Þ
bij xi xj
i¼1;i\j i¼2
The approximation for the true functional relationship between the viscosity of the aqueous composite polymer solution and the set of independent factors is thus determined where b0 is the offset term, bi is the linear effect, bii is the squared effect, bij represents the interaction effect, and the xi are the coded variables. Design Expert 6 was used to analyze the variations and to determine the effects of the various factors, their interactions, as well as the statistical parameters of the model. The response of the independent factors was illustrated by three-dimensional surface and contour plots. ANOVA was used to analyze the response, and the polynomial regression model was constructed based on the significant terms with p \ 0.05. The quality of fit of Table 1 Levels of variables tested in the Central Composite Design Variables
Range and levels –a
–1
0
?1
?a
x1: magnesium electrolyte (gL-1)
3.5
6.0
10.0
14.0
16.5
x2: polyacrylamide (gL-1)
3.5
6.0
10.0
14.0
16.5
294.84
298.00
303.00
308.00
311.16
x3: temperature (K)
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1
2
12
13
20
2
2
1
11
19
1
10
2
1
9
2
1
8
18
1
7
17
1
6
2
1
5
16
1
4
2
1
3
2
1
2
14
1
1
15
Block
Runs
–1
0
0
0
0
0
0
?a
–a
0
0
0
0
?1
–1
?1
–1
?1
–1
?1
–1
0
0
0
0
?a
–a
0
0
0
0
0
0
?1
?1
–1
–1
?1
?1
–1
–1
0
0
?a
–a
0
0
0
0
0
0
0
0
?1
?1
?1
?1
-1
–1
–1
10.0
10.0
10.0
10.0
10.0
10.0
16.5
3.5
10.0
10.0
10.0
10.0
14.0
6.0
14.0
6.0
14.0
6.0
14.0
6.0
Magnesium electrolyte, gL-1 (x1)
10.0
10.0
10.0
10.0
16.5
3.5
10.0
10.0
10.0
10.0
10.0
10.0
14.0
14.0
6.0
6.0
14.0
14.0
6.0
6.0
PAM, gL-1 (x2)
303.00
303.00
311.16
294.84
303.00
303.00
303.00
303.00
303.00
303.00
303.00
303.00
308.00
308.00
308.00
308.00
298.00
298.00
298.00
298.00
Temperature, K (x3)
1.0087
1.0087
1.0067
1.0103
1.0105
1.0078
1.0138
1.0037
1.0087
1.0088
1.0087
1.0088
1.0113
1.0058
1.0102
1.0041
1.0136
1.0081
1.0123
1.0060
MCPAM density, gcm-3
Temperature, K (x3)
Magnesium electrolyte, gL-1 (x1)
PAM, gL-1 (x2)
Response
Factors (coded unit)
8.8051
8.5257
8.2033
9.1261
28.3984
2.1981
9.4583
8.4921
8.6909
8.6790
8.6728
8.6871
19.4571
19.0429
4.2339
3.7916
20.5833
20.0610
4.5705
4.0220
MCPAM viscosity, cP (y)
2.1753
2.1431
2.1045
2.2111
3.3463
0.7876
2.2469
2.1391
2.1623
2.1609
2.1602
2.1618
2.9682
2.9467
1.4431
1.3328
3.0245
2.9988
1.5196
1.3918
MCPAM ln g
1.0106
1.0105
1.0084
1.0118
1.0117
1.0103
1.0141
1.0069
1.0105
1.0106
1.0105
1.0105
1.0123
1.0089
1.0105
1.0055
1.0151
1.0113
1.0127
1.0076
MHPAM density, gcm-3
Table 2 Central Composite Design for the viscosity g of aqueous magnesium electrolyte–polyacrylamide composite polymer solutions
18.8536
18.5560
17.7930
19.7750
51.0077
3.6632
18.9722
16.8237
18.815
18.4745
18.8432
18.5270
38.1862
36.7260
7.6569
7.3601
40.4199
38.3437
8.0235
7.6021
MHPAM viscosity, cP (y)
2.9367
2.9208
2.8788
2.9844
3.9320
1.2983
2.9430
2.8228
2.9347
2.9164
2.9362
2.9192
3.6425
3.6035
2.0356
1.9961
3.6993
3.6466
2.0824
2.0284
MHPAM ln g
J Solution Chem (2013) 42:27–43 31
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the model was determined by least-squares. To determine the adequacy of the proposed polynomial regression model, a validation test involving a diagnostic plot of predicted viscosity versus experimental viscosity was carried out. 2.5 Transmission Electron Microscopy (TEM) The aqueous solution of MCPAM and MHPAM composite polymers were analyzed by TEM (Philips, CM12), in order to investigate the formation of the composite polymer. One drop of the aqueous composite polymer solution was carefully introduced on the copper grid and dried with filter paper. The sample-coated copper grid was then placed under TEM for image viewing.
3 Results and Discussion 3.1 Solution Viscosity Model Fitting The effects of the independent factors such as concentration of the magnesium electrolyte, concentration of polyacrylamide, and temperature of the solution were taken into account for CCD. Viscosity was taken as the response of the design as given in Table 2. The viscosity values of aqueous MCPAM and MHPAM composite polymer solutions range from 2.1981 to 28.3984 cP and 3.6632 to 51.0077 cP, respectively. The ratios of the highest viscosity to the lowest viscosity for both aqueous MCPAM and MHPAM composite polymer solutions are greater than a factor of 10. Therefore, a transformation of the response is required. The response of the second-order polynomial regression model was transformed by taking natural logarithm as shown in Eq. 4: ln g ¼ b0 þ
k X i¼1
b i xi þ
k X i¼1
bii x2i þ
k¼1 X k X
bij xi xj
ð4Þ
i¼1;i\j i¼2
For the corresponding fitting of the explanatory models and the variations of ln g, the sum of squares of the sequential model was analyzed. These analyses indicate that adding terms up to quadratic significantly improves the ln g model as shown in Tables 3 and 4, for aqueous MCPAM and MHPAM composite polymer solutions, respectively. Therefore, the second-order polynomial regression model is proposed to be the most appropriate model for the independent factors [18]. Regression analysis was used to fit the polynomial regression model. ANOVA was used to investigate the statistical significance of the terms. The second-order polynomial regression model was constructed for ln g values of the aqueous MCPAM and MHPAM composite polymer solutions, based on the coded factors with their respective estimated regression coefficients given in Tables 5 and 6, respectively, along with their corresponding statistical significance (p value). The significant linear, quadratic and interaction terms were taken into account for the model fitting. For the MCPAM composite polymer, linear terms x1, x2 and x3, where x1 represents the concentration of magnesium chloride, x2 represents the concentration of polyacrylamide and x3 represents the temperature of solution, quadratic terms x21, x22 and x23, and the interaction term x1x2 were found to be significant at the 95 % confidence level. Therefore, ln g values of the aqueous MCPAM composite polymer solutions were fitted with the following equation:
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ln g ¼ 54:52173 0:010828ðx1 Þ þ 0:23516ðx2 Þ 0:35355ðx3 Þ þ 0:00171988ðx1 Þ2 0:00123445ðx2 Þ2 þ0:000573069ðx3 Þ2 0:00149186ðx1 x2 Þ
ð5Þ
The viscosities of the aqueous MHPAM composite polymer solutions were fitted with the following equation: ln g ¼ 48:70986 þ 0:00715253ðx1 Þ þ 0:33392ðx2 Þ 0:31514ðx3 Þ 0:00661797ðx2 Þ2 þ 0:000511324ðx3 Þ2 ð6Þ where x1 represents the concentration of magnesium hydroxide, x2 represents the concentration of polyacrylamide, and x3 represents the temperature of solution. Linear terms: x1, x2 and x3 quadratic terms: x22 and x23 were found to be significant at the 95 % confidence level. Lack of fit tests were carried out to determine the success or failure of a model to represent the experimental data, from which some points were not included in the regression or variations in the model that cannot be accounted for by random error [30]. Insignificant lacks of fit were found for both solution viscosity models, indicating that the polynomial regression models are sufficient to represent the experimental results. Coefficient of determination, R2, is the proportion of variation in the response attributed to the model rather than to random error. The R2 values for the viscosity models of aqueous MCPAM and MHPAM composite polymer solutions are 0.9995 and 0.9996, respectively. A high R2 value shows the appropriateness of the model to explain the relation between variables [18].
3.2 Effects of Concentrations of Magnesium Electrolyte and Polyacrylamide The effects of concentrations of the magnesium electrolyte and polyacrylamide on the ln g values of aqueous MCPAM and MHPAM composite polymer solutions are shown in Fig. 1a and b, respectively. Figure 1a and b reveal that, with the increase of the concentrations of magnesium chloride and polyacrylamide, the ln g values of the aqueous MCPAM composite polymer solution increase in a quadratic manner. A significant interaction term was found in Eq. 5 for the concentrations of magnesium chloride and polyacrylamide in the aqueous solution. Magnesium chloride is completely miscible with polyacrylamide as magnesium chloride dissociates into Mg2? and Cl- ions when it is blended with an aqueous polyacrylamide solution. The Cl- ions approach the Table 3 Sequential model sum of squares of viscosity of aqueous MCPAM composite polymer solutions Source
df
Sum of squares
Mean square
p Value
Mean
1
94.29
Linear
3
8.19
2.73
Interaction
3
0.004709
0.001570
Quadratic
3
0.019
0.006268
0.0009
Cubic
4
0.00007416
0.00001854
0.9985
0.003916
0.0007832
Residual Total
5 20
102.51
94.29 \0.0001 0.5043
5.13
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Table 4 Sequential model sum of squares of viscosity of aqueous MHPAM composite polymer solutions Source
df
Sum of squares
Mean square
Mean
1
163.35
163.35
Linear
3
8.69
2.90
Interaction
3
0.0001538
0.00005126
Quadratic
3
0.15
0.052
Cubic
4
0.0009133
0.0002283
Residual
5
0.002404
0.0002808
Total
20
172.20
p Value
\0.0001 0.9996 \0.0001 0.7548
8.61
Table 5 ANOVA and regression coefficients of the second-order polynomial regression model for the viscosity of aqueous MCPAM composite polymer solutions Source
DF
Model
7
Coefficient ?54.52173
Sum of squares
p Value
8.21
\0.0001
Linear x1
1
–0.010828
0.016
\0.0001
x2
1
?0.23516
8.16
\0.0001
x3
1
–0.35355
0.013
0.0001
x21
1
?0.00171988
0.010
0.0003
x22
1
–0.00123445
0.005152
0.0035
x23
1
?0.000573069
0.002711
0.0213
1
–0.00149186
0.004558
0.0052
Quadratic
Interaction x1 x2 Residual
11
0.004141
Lack of fit
7
0.003618
Pure error
4
0.0005225
Total
19
0.1009
8.22 SD = 0.019
R2 = 0.9995
R2 (adj.) = 0.9992
x1: concentration of magnesium chloride; x2: concentration of polyacrylamide; x3: temperature of solution
electronegative nitrogen atom of polyacrylamide while Mg2? ion appears to be a free ion as shown in Fig. 2a. In comparison with aqueous MHPAM composite polymer solutions, aqueous MCPAM composite polymer solutions have lower ln g values and this is in agreement with the finding of Xin el al. [31], where addition of a metal chloride salt caused a decrease the viscosity of the aqueous polyacrylamide solution. This is due to the polyacrylamide aggregate becoming clumped from addition of magnesium ion, which results in a decrease of polyacrylamide hydrodynamic volume and in turn results in a decrease of the solution viscosity [32]. For the MHPAM composite polymer system, there could also be a hydrogen bonding network that is formed between magnesium hydroxide and the electronegative nitrogen atom of polyacrylamide as shown in Fig. 2b. The hydrogen bonding network
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Table 6 ANOVA and regression coefficients of the second-order polynomial regression model for the viscosity of aqueous MHPAM composite polymer solutions Source
df
Model
7
Coefficient ?48.70986
Sum of squares
p Value
8.84
\0.0001
Linear x1
1
?0.00715253
0.011
\0.0001
x2
1
?0.33392
8.67
\0.0001
x3
1
–0.31514
0.009268
\0.0001
x22
1
–0.00661797
0.15
\0.0001
x23
1
Quadratic
Residual
0.000511324
11
0.002169
Lack of fit
7
0.003492
Pure error
4
0.0004412
Total
0.0190
0.003933
19
0.1191
8.85 SD = 0.017
R2 = 0.9996
R2 (adj.) = 0.9994
x1: concentration of magnesium hydroxide; x2: concentration of polyacrylamide; x3: temperature of solution
formed between magnesium hydroxide and polyacrylamide thus gives rise to the ln g values of the aqueous solutions. However, only a linear effect was observed for the concentration of magnesium hydroxide whereas the ln g values of the aqueous MHPAM composite polymer solutions increase linearly with the concentration of magnesium hydroxide. Insignificant interaction was found in Eq. 6 for magnesium hydroxide and polyacrylamide in the aqueous solutions. This is because magnesium hydroxide is only miscible with polyacrylamide at low concentrations as the hydrogen bonding network is formed between magnesium hydroxide and polyacrylamide. However, with an increase of magnesium hydroxide concentration, the domination of magnesium hydroxide in the blend leads to the immiscibility of magnesium hydroxide with polyacrylamide in the aqueous composite polymer solution. Thus, miscibility of the magnesium electrolyte with polyacrylamide in the aqueous solution can be observed based on the significance of the interaction terms in the polynomial regression model. The ln g values of MCPAM and MHPAM systems increase in a quadratic manner with the concentration of polyacrylamide. This is because at low concentrations of polyacrylamide, polyacrylamide chains have little chance to interact with each other, and thus the viscosity is observed to be relatively low. With increase of the polyacrylamide concentration, polyacrylamide chains begin to interact between each other and intermolecular associations come into effect, which then leads to a drastic increase in viscosity [33]. 3.3 Effect of Temperature The effects of temperature on the ln g values of aqueous MCPAM and MHPAM composite polymer solution systems are shown in Figs. 3 and 4, respectively. The viscosity of polyacrylamide-based solution is very sensitive to changes of temperature [34] and it was also found that temperature has a significant effect on the solution viscosity as seen in
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(a) 2.9527 2.54431
ln (Viscosity) (cP)
Fig. 1 Effect of the concentrations of magnesium electrolytes and polyacrylamide on the values of the natural logarithm of viscosity of a the aqueous MCPAM composite polymer solutions, and b the aqueous MHPAM composite polymer solutions
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2.13591 1.72751 1.31911
14.00 14.00 12.00 12.00 10.00
10.00
PAM (g/L) 8.00
8.00 6.00
MgCl2 (g/L)
6.00
(b) 3.64588
ln (Viscosity) (cP)
3.22845 2.81101 2.39358 1.97615
14.00 14.00 12.00 12.00 10.00
10.00
PAM (g/L) 8.00
8.00 6.00
Mg(OH)2 (g/L)
6.00
Tables 5 and 6 for MCPAM and MHPAM composite polymers, respectively. The ln g values of the aqueous composite polymer solutions decrease with increasing temperature, which is in agreement with the finding of Xin et al. [31]. From the polynomial regression models, as shown in Eqs. 5 and 6, temperature has a quadratic effect on the ln g values of aqueous composite polymer solutions at different concentrations of magnesium electrolytes and polyacrylamide. The temperature has a non-linear and inverse effect on the ln g values of aqueous composite polymer solutions. This was explained by Eyring [35], who
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37
Fig. 2 Chemical structure of a the MCPAM composite polymer and b MHPAM the composite polymer
showed that the viscosity of a liquid has a non-linear and inverse relationship with the temperature: Nhq ðDG g¼ ð7Þ e RT Þ M where g = viscosity of solution, N = Avogadro’s number, h = Planck’s constant, q = density of the liquid, M = molecular weight, G* = Gibbs energy of activation, R = gas constant and T = temperature (K). By taking the logarithm of Eq. 7, the relationship of ln g with temperature is shown as Eq. 8, where, with increase of temperature, the ln g values will decrease or vice versa, which is in agreement with our empirical polynomial regression model as shown by Eqs. 5 and 6 Nhq DG þ : ð8Þ ln g ¼ ln M RT
3.4 Validation of the Developed Model Validation of the developed polynomial regression models for ln g of aqueous magnesium electrolyte–polyacrylamide composite polymer solutions is necessary to ensure the agreement of the predicted ln g values with the experimental results. Predicted ln g values versus their corresponding experimental ln g values were plotted in order to determine the adequacy of the developed polynomial regression model for the solution viscosity of aqueous composite polymer solutions. The diagnostic plots plotted for ln g of aqueous
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(a) 2.2598 2.22589
ln (Viscosity) (cP)
Fig. 3 Effect of the temperature of solution and concentration of the magnesium electrolyte on the values of the natural logarithm of viscosity of a the aqueous MCPAM composite polymer solutions, and b the aqueous MHPAM composite polymer solutions
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2.19197 2.15805 2.12414
308.00 14.00
305.50 12.00 303.00
Temperature (K)
10.00
300.50
MgCl2 (g/L)
8.00
298.00 6.00
(b) 2.98466
ln (Viscosity) (cP)
2.95717 2.92969 2.9022 2.87471
308.00 14.00
305.50 12.00 303.00
Temperature (K)
10.00
300.50
8.00
Mg(OH)2 (g/L)
298.00 6.00
MCPAM and MHPAM solutions are shown in Fig. 5a and b, respectively. In these plots, each of the observed results is compared to the predicted value calculated from the model. Both diagnostic plots show least-squares value (R2) of 0.9995, indicating that the developed polynomial regression model for ln g of the aqueous composite polymer solutions fits the experimental results.
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(a) 2.96012 2.55336
ln (Viscosity) (cP)
Fig. 4 Effect of the temperature of solution and concentration of polyacrylamide on the values of the natural logarithm of viscosity of a the aqueous MCPAM composite polymer solutions, and b the aqueous MHPAM composite polymer solutions
39
2.14659 1.73982 1.33305
308.00 14.00 305.50 12.00 303.00
10.00
Temperature (K) 300.50 298.00
8.00
PAM (g/L)
6.00
(b) 3.65642
ln (Viscosity) (cP)
3.24011 2.8238 2.40749 1.99117
308.00 14.00 305.50 12.00 303.00
Temperature (K)
10.00
300.50 298.00
8.00
PAM (g/L)
6.00
3.5 Transmission Electron Microscopy (TEM) The TEM images of aqueous solution of MCPAM and MHPAM composite polymers are shown in Fig. 6a and b, respectively. The black spots observed on the polyacrylamide
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Predicted ln (Viscosity) (cP)
(a)
3.5
2
R = 0.9995
3.0
2.5
2.0
1.5
1.0
0.5 0.5
1.0
1.5
2.0
2.5
3.0
3.5
3.5
4.0
Actual ln (Viscosity) (cP)
(b)
4.0
2
Predicted ln (Viscosity) (cP)
R = 0.9995 3.5
3.0 2.5 2.0 1.5
1.0 1.0
1.5
2.0
2.5
3.0
Actual ln (Viscosity) (cP) Fig. 5 Diagnostic plot of the predicted values of the natural logarithm of viscosity versus experimental values the natural logarithm of viscosity of a the aqueous MCPAM composite polymer solutions, and b the aqueous MHPAM composite polymer solutions
chain are the magnesium ions, where magnesium chloride, which is fully miscible with polyacrylamide, is impregnated into the polyacrylamide chains. Relatively poorer miscibility was observed for the MHPAM composite polymer in Fig. 6b where magnesium hydroxide appears as larger particles compared to those of magnesium chloride in the MCPAM composite polymer. Magnesium hydroxide particles were observed to be absorbed on the surface of polyacrylamide chain through hydrogen bonding that occurs between magnesium hydroxide and polyacrylamide.
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Fig. 6 TEM image of a an aqueous MCPAM composite polymer solution, and b an aqueous MHPAM composite polymer solution
4 Conclusions Response Surface Methodology (RSM) was used to investigate the viscometric properties of the aqueous magnesium electrolyte–polyacrylamide composite polymer solutions. The experiments were designed according to CCD with three independent factors, i.e. the concentration of magnesium electrolyte (magnesium chloride and magnesium hydroxide), concentration of polyacrylamide, and temperature of the solution. All independent factors tested were found to be significant. A second-order polynomial model was developed to model the viscosity change using ANOVA and regression analysis. The ln g values of aqueous MHPAM composite polymer solutions are higher compared to those of aqueous MCPAM composite polymer solutions due to the hydrogen bonding between magnesium hydroxide and polyacrylamide. TEM images show that magnesium chloride was
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impregnated on the polyacrylamide chain while magnesium hydroxide was just adsorbed on the surface of the polyacrylamide chain. Acknowledgments The authors gratefully acknowledge financial support from Universiti Sains Malaysia in the form of a postgraduate fellowship, as well as Research University grant, which have resulted in this paper.
References 1. Wang, Y., Gao, B., Yue, Q., Wei, J., Li, Q.: The characterization and flocculation efficiency of composite flocculant iron salts–polydimethyldiallylammonium chloride. Chem. Eng. J. 142, 175–181 (2008) 2. Tang, H., Shi, B.: The characteristics of composite flocculants synthesized with inorganic polyaluminium and organic polymers. In: Hahn, H., Hoffmann, E., Odegaard, H. (eds.) Chemical Water and Wastewater Treatment VII. Proceedings of the 10th Gothenburg Symposium 2002. June 17–19, pp. 17–28. Gothenburg, Sweden (2002) 3. Yang, W.Y., Qian, J.W., Shen, Z.Q.: A novel flocculant of Al(OH)3–polyacrylamide ionic hybrid. J. Colloid Interface Sci. 273, 400–405 (2004) 4. Moussas, P., Zouboulis, A.I.: A new inorganic–organic composite coagulant, consisting of polyferric sulphate (PFS) and polyacrylamide (PAA). Water Res. 43, 3511–3524 (2009) 5. Gao, B., Wang, Y., Yue, Q.-Y., Wei, J.-C., Li, Q.: Color removal from simulated dye water and actual textile wastewater using a composite coagulant prepared by polyferric chloride and polydimetyldiallylammonium chloride. Sep. Purif. Technol. 54, 157–163 (2007) 6. Gao, B.-Y., Wang, Y., Yue, Q.-Y., Wei, J.-C., Li, Q.: The size and coagulation behaviour of a novel composite inorganic–organic coagulant. Sep. Purif. Technol. 62, 544–550 (2008) 7. Gao, B.-Y., Wang, Y., Yue, Q.-Y.: The chemical species distribution of aluminium in composite flocculants prepared from polyaluminium chloride (PAC) and polydimethyldiallylammonium chloride (PDADMAC). Acta Hydrochim. Hydrobiol. 33, 365–371 (2005) 8. Lee, K.E., Teng, T.T., Morad, N., Poh, B.T., Hong, Y.F.: Flocculation of kaolin in water using novel calcium chloride–polyacrylamide (CaCl2–PAM) hybrid polymer. Sep. Purif. Technol. 75, 346–351 (2010) 9. Lee, K.E., Teng, T.T., Morad, N., Poh, B.T., Mahalingam, M.: Flocculation activity of novel ferric chloride (FeCl3–PAM) hybrid polymer. Desalination 266, 108–113 (2011) 10. Lee, K.E., Khan, I., Morad, N., Teng, T.T., Poh, B.T.: Thermal behavior and morphological properties of novel magnesium salt–polyacrylamide composite polymers. Polym. Compos. 32, 1515–1522 (2011) 11. Lee, K.E., Khan, I., Morad, N., Teng, T.T., Poh, B.T.: Physicochemical and rheological properties of novel magnesium salt–polyacrylamide composite polymers. J. Dispers. Sci. Technol. (2011). doi:10.1080/01932691.2011.605674 12. Idris, A., Kormin, F., Noordin, M.Y.: Application of response surface methodology in describing the performance of thin film composite membrane. Sep. Purif. Technol. 49, 271–280 (2006) 13. Wang, J.-P., Chen, Y.-Z., Ge, X.-W., Yu, H.-Q.: Optimization of coagulation-flocculation process for a paper-recycling wastewater treatment using response surface methodology. Colloids Surf. A 302, 204–210 (2007) 14. Ahmad, A.L., Wong, S.S., Teng, T.T., Zuhairi, A.: Optimization of coagulation-flocculation process for pulp and paper mill effluent by response surface methodological analysis. J. Hazard. Mater. 145, 162–168 (2007) 15. Khayet, M., AbuSeman, M.N., Hilal, N.: Response surface modeling and optimization of composite nanofiltration modified membranes. J. Membr. Sci. 349, 113–122 (2010) 16. Aktas, N.: Optimization of biopolymerization rate by response surface methodology. Enzyme Microb. Technol. 37, 441–447 (2005) 17. Ikegwu, O.J., Ekwu, F.C.: Use of response surface methodology in predicting the apparent viscosity of ‘Achi’ Brachystegia spp Flour. Pak. J. Nutr. 9, 720–723 (2010) 18. Koocheki, A., Taherian, A.R., Razavi, S.M.A., Bostan, A.: Response surface methodology of optimization extraction yield, viscosity, hue and emulsion stability of mucilage extracted from Lepidium perfoliatum seeds. Food Hydrocoll. 23, 2369–2379 (2009)
123
J Solution Chem (2013) 42:27–43
43
19. Karazhiyan, H., Razavi, S.M.A., Philips, G.O.: Extraction optimization of a hydrocolloid extract from cress seed (Lepidium sativum) using response surface methodology. Food Hydrocoll. 25, 915–920 (2011) 20. Iwe, M.O., Wolters, I., Gort, G., Stolp, W., van Zuilichem, D.J.: Behaviour of gelatinization and viscosity in soy–sweet potato mixtures by single screw extrusion: a response surface analysis. J. Food Eng. 38, 369–379 (1998) 21. Ibanoglu, S., Ainsworth, P.: Application of response surface methodology for studying the viscosity changes during canning of tarhana, a cereal-based food. J. Food Eng. 64, 273–275 (2004) 22. Lee, K.E., Poh, B.T., Morad, N., Teng, T.T.: Synthesis and characterization of hydrophobically modified cationic acrylamide copolymer. Int. J. Polym. Anal. Charact. 13, 95–107 (2008) 23. Lee, K.E., Poh, B.T., Morad, N., Teng, T.T.: Synthesis and characterization of hydrophobically modified cationic polyacrylamide with low concentration of cationic monomer. J. Macromol. Sci. Pure. 46, 240–249 (2009) 24. Lee, K.E., Morad, N., Teng, T.T., Poh, B.T.: Kinetics and in situ rheological behaviour of acrylamide redox polymerization. J. Disper. Sci. Technol. 33, 387–395 (2012) 25. Lee, K.E., Morad, N., Teng, T.T., Poh, B.T.: Comparative study on the effectiveness of hydrophobically modified cationic polyacrylamide groups in the flocculation of kaolin. Desalination 270, 206–213 (2011) 26. Tan, B.H., Teng, T.T., Omar, A.K.M.: Removal of dyes and industrial dye wastes by magnesium chloride. Water Res. 34, 597–601 (2000) 27. Gao, B.-Y., Yue, Q.-Y., Wang, Y., Zhou, W.-Z.: Color removal from dye-containing wastewater by magnesium chloride. J. Environ. Manage. 82, 167–172 (2007) 28. Semerjian, L., Ayoub, G.M.: High-pH-magnesium coagulation–flocculation in wastewater treatment. Adv. Environ. Res. 7, 389–403 (2003) 29. Weast, R.C.: Handbook of Chemistry and Physics, 57th edn. CRC Press, Ohio (1976) 30. Montgomery, D.C.: Design and Analysis of Experiments, 5th edn. Wiley, New York (2001) 31. Xin, X., Xu, G., Wu, D., Li, Y., Cao, X.: The effect of CaCl2 on the interaction between hydrolyzed polyacrylamide and sodium stearate: rheological property study. Colloids Surf. A 305, 138–144 (2007) 32. Zhang, L., Zhang, D., Jiang, B.: The rheological behavior of salt tolerant polyacrylamide solutions. Chem. Eng. Technol. 29, 395–400 (2006) 33. Poh, B.T., Ong, B.T.: Dependence of viscosity of polystyrene solutions on molecular weight and concentration. Eur. Polym. J. 20, 975–978 (1984) 34. Shin, S., Cho, Y.I.: Temperature effect on the non-Newtonian viscosity of an aqueous polyacrylamide solution. Int. Commun. Heat Mass Transf. 20, 831–844 (1993) 35. Eyring, H.: Viscosity, plasticity, and diffusion as examples of absolute reaction rates. J. Chem. Phys. 4, 283–291 (1936)
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