3.1. Characterization
3.1.1 Fourier Transform Infrared Spectra Analysis
The Fourier transform infrared spectrum of the chitosan and the modified chitosan before adsorption is given in Fig. 1a and 1b respectively. From Fig. 1a, it can be observed from that the functional groups responsible for the adsorption on the surface of the adsorbent are the hydroxyl group and amino group at 3427cm− 1. The peaks observed at 3072, 2911, and 2734 cm− 1 were assigned to the stretching vibrations of C–H bond in methyl group while 1383 cm− 1 could be associated to the bending vibration of C–H bond in the methyl group and the band at 878 cm-1 can represents the symmetrical vibration in a chain of P-O-P and to P-C phosphorus-containing compound as seen in the figure. These functional groups are negatively charged and can attract positively charged lead metal ions. While Fig. 1b, it can be observed that the functional groups responsible for adsorption on the surface of the adsorbent are, a hydroxyl group and amino group at 3427 cm− 1region.The peaks observed at 3072, 2911 and 2734 cm− 1 were assigned to the stretching vibrations of C–H bond in methyl group while 1383 cm− 1 was assigned to the bending vibration of C–H bond in methyl group[35].
From Fig. 2a and 2b, the decrease in the number of peaks after adsorption, formation of new absorption bands, the change in absorption intensity, and the shift in wavenumber of functional groups could be attributed to complexation between metal ions and binding sites of adsorbents. The binding mechanism involved sharing of electron pair between electron donor atoms (O and N) and metal ion. FTIR suggests that both hydroxyl and amine groups are indeed the main adsorption sites in the chitosan and the modified chitosan(Kyzas and Bikiaris 2015).
Table 3
Kinetics and Isotherm Models Equations Explored
Kinetic Models
|
Equations
|
Isotherm Models
|
Equations
|
Pseudo- First Order
|
\(\frac{{d}_{qt}}{dt}= {K}_{1}\left({q}_{e}-{q}_{t}\right)\)
|
Langmuir
|
\(\frac{{C}_{e}}{{q}_{e}}=\frac{1}{{K}_{L}{q}_{m}}+\frac{{C}_{e}}{{q}_{e}}\)
|
Pseudo- Second Order
|
\(\frac{dqt}{dt}= {K}_{2}{\left({q}_{e}-{q}_{t}\right)}^{2}\)
|
Freundlich
|
\(\text{log}{q}_{e}=\text{log}K+\frac{1}{n} \times \text{log}{C}_{e}\)
|
Elovich
|
\(\left(\frac{dqt}{dt}\right)=a\text{exp}\left(-bqt\right)\)
|
Temkin
|
\({q}_{e}\) = Bln\({A}_{T}\) + Bln\({C}_{e}\)
|
Bhattacharya and Venkobachar
|
\(\text{ln}\left(1-Ut\right)=-\beta t\)
|
Dubinin–Radushkevich
|
ln\({q}_{e}\)= ln\({q}_{o}\)- Kdε2
|
Weber and Morris
|
\({q}_{t}\) = Kid t1/2 + C
|
Lineweaver-Burk
|
\(\frac{1}{{q}_{e}}= \frac{1}{{q}_{m}}+ \frac{1}{{q}_{m}{K}_{L}{C}_{e}}\)
|
Boyd Model
|
Log(\({q}_{m}\)– \({q}_{t}\)) = log(\({q}_{m}\)) –\(\left(\frac{{D}_{i}}{2.303}\right)\)
|
|
|
3.1.2. Scanning Electron Microscope (SEM)
Figures 3a, 3b, 4a and 4b show that the investigated adsorbent contains many pores in which metal ions can be trapped and adsorbed. Because of its uniform surface area, the material's shape can enhance metal adsorption[20]. Furthermore, SEM analysis revealed the significant changes on the surface morphology after modification with phosphoric acid as shown by Fig. 4a. The surface of the phosphoric acid modified chitosan is rougher than that of the chitosan. So based on the morphology, it can be concluded that this material presents an adequate morphological profile to retain metal ions.
3.1.3. X-ray Diffraction (XRD) Analysis
XRD patterns for the chitosan and modified chitosan are shown in Figs. 5a and 5b. The obtained characteristic diffraction peaks for these biosorbents are consistent with those in the literature (Kyzas and Deliyanni, 2013; Balau et al., 2004). In the X–ray diffraction spectrogram of chitosan, a broad peak at 2θ = 20.2° present due to the amorphous state of chitosan with other three characteristic peaks at 20.6°, 20.9° and 30.4° attributable to the indices were observed. In the spectrogram for the phosphoric acid modified chitosan, only a broad peak at 2θ = 20° was observed. The average crystallite size, D (nm), ofthe modified chitosan was calculated about 10.5 nm, using the Debye–Scherrer equation [37]:
$$D=\frac{{K}_{s}.{\lambda }}{B.cos{\theta }}$$
6
Where Ks is a constant (Ks = 0.9 for CuKa), λ (nm) is the wavelength (0.15405 nm for CuKa), B is the peak width of half–maximum (rad) and θ is the diffraction angle.
Phosphoric acid reduces the crystallinity of the raw chitosan and improves it via cross linking, thereby making modified chitosan less crystalline as confirmed by XRD analysis[39]. Crystallinity lowers adsorption capacity because there is less surface area and few open active sites for metal binding compared to amorphous adsorbents with large surface area and more open active sites. This reduction in crystallinity enhances the probability for an adsorbate to interact with the adsorbent due to loosen inter-chain structure and bonding, thus expose more active sites for adsorption which increased the adsorption capacity of the modified chitosan [40].
3.1.4. Differential Scanning CalorimetryThermogram
The thermogram for the chitosan and the modified chitosan are given in Fig. 6a and 6b respectively. From Fig. 6a, a broad endotherm over the temperature range of 28 and 98°C (with peak at 83.42°C) was seen, followed by a sharp endotherm which peaked at 204.71°C. The diffuse endotherm of chitosan which peaked at 83.42°C can be ascribed to loss of the absorbed water (Dhawade and Jagtap 2012; Jana et al., 2015)and 0.009824J of heat per milligram sample of the adsorbent was absorbed in the process. The second endotherm can be ascribed to polymer melting. The area under the curve (0.0021143 J/mg) represents the latent heat of melting while the peak (204.71°C) represents the melting point of the polymer [41]. From Fig. 6b, the first endotherm of the modified chitosan corresponds to relaxation transition, which peaked at 193.81oC. Endothermic relaxation is a second order reaction just like glass transition [43]. Meanwhile, second endotherm which can be attributed to polymer (phosphoric acid modified chitosan) melting picked at 240oC. It is obvious that the modified chitosan demonstrated significant stability over a wide temperature range (80-170oC).
3.2. Development of Model Equation
The design matrix containing the studied factors, their ranges and the response which is the removal efficiency (%) of Pb(II), (Ypmc) and (\({\mathbf{Y}}_{\mathbf{c}\mathbf{s}}\)) is presented in Table 2. The results obtained were then evaluated with CCD in RSM for the development of the model’s regression equation. A quadratic second order polynomial expression as suggested by the software fitted the data suitably. The model equation was selected in conformity with the sequential model sum of the square that is based on the highest order polynomial where the additional terms of the model are significant, and the model is not aliased. Both the regression coefficients (R2) and adjusted R2wereused to validate the quality of the proposed models; values closer to 1.0 confirm a good agreement between the predicted and experimental data. Thus, the correlation between experimental and predicted response is evident as indicated by the model’s R2 and adjusted R2 values of 0.94 and 0.90, which were within the desirability range. The final model’s equation for the removal efficiency of lead by modified chitosan, (\({\mathbf{Y}}_{\mathbf{p}\mathbf{m}\mathbf{c}}\)) and raw chitosan, (\({\mathbf{Y}}_{\mathbf{c}\mathbf{s}}\)) are given by Eqs. (7) and (8) respectively:
\({\mathbf{Y}}_{\mathbf{p}\mathbf{m}\mathbf{c}}\) = 74.57829+6.1607035*A + 8.2275766*B + 2.3218341*C-0.104926*D + 0.2963844*E-3.320276*A2-5.156986*B2-2.437222*C2-2.73161*D2-0.116199*E2 + 2.2462946*AB-1.761432*AC + 1.3925446*AD-2.74808*AE + 2.3489324*BC + 2.7949554*BD-0.14192*BE + 2.1239324*CD-0.725443*CE + 1.1368304*DE (7)
\({\mathbf{Y}}_{\mathbf{c}\mathbf{s}}\) = 63.72168 + 6.603598*A + 7.481349*B -0.31528*C-1.61259D-0.36278*E-0.92096*A2-4.42379*B2-0.5086*C2 + 1.594576*D2-0.8856*E2 + 2.963686*AB-0.95303*AC + 1.135561*AD-2.39256*AE + 1.960528*BC + 1.990689*BD-1.91994*BE + 1.752403*CD-0.00822*CE + 1.208189*DE (8)
The cooperative and counter effects of the studied factors were elucidated by the negative and positive coefficients before the terms in the model equation. A negative coefficient value implies that the term negatively affects Pb2+adsorption (i.e. the removal efficiency decreases), whereas a positive coefficient values mean that the term increase Pb2+adsorption in the tested range (Muluh, 2017).
Table 4
Statistical parameters obtained from the analysis of variances (ANOVA) for the
models for Pb (II) Ion % removal from waste water by the raw and modified chitosan.
Statistical Parameters
|
chitosan
|
Modified chitosan
|
Std. Dev.
|
4.95
|
4.88
|
Mean
|
59.32
|
62.72
|
Coefficient of variation, CV
|
8.34
|
7.78
|
PRESS
|
2684.98
|
2762.28
|
R- squared (R2)
|
0.91
|
0.92
|
R2 adjusted
|
0.84
|
0.87
|
Predicted R2
|
0.77
|
0.69
|
Adequate Precision
|
19.39
|
19.90
|
Table 5a: Analysis of variances (ANOVA) and lack-of- fit test for response surface quadratic model for removal of Pb (II) ions from aqueous solution by the modified chitosan
Source
|
Sum of squares
|
DF
|
Mean square
|
F-Value
|
Prob.>F
|
Comment
|
Model
|
8209.80
|
20
|
410.49
|
17.25
|
< 0.0001
|
Significant
|
A
|
1641.90
|
1
|
1641.90
|
69.00
|
< 0.0001
|
|
B
|
2928.41
|
1
|
2928.40
|
123.07
|
< 0.0001
|
|
C
|
228.50
|
1
|
228.49
|
9.60
|
0.0043
|
|
D
|
0.47
|
1
|
0.48
|
0.02
|
0.8885
|
|
E
|
3.80
|
1
|
3.80
|
0.16
|
0.6923
|
|
A2
|
612.18
|
1
|
612.18
|
25.73
|
< 0.0001
|
|
B2
|
1476.80
|
1
|
1476.80
|
62.07
|
< 0.0001
|
|
C2
|
330.25
|
1
|
330.24
|
13.88
|
0.0008
|
|
D2
|
414.35
|
1
|
414.35
|
17.41
|
0.0002
|
|
E2
|
0.75
|
1
|
0.75
|
0.03
|
0.8603
|
|
AB
|
161.19
|
1
|
161.19
|
6.77
|
0.0144
|
|
AC
|
96.43
|
1
|
96.43
|
4.05
|
0.0535
|
|
AD
|
61.95
|
1
|
61.95
|
2.60
|
0.1174
|
|
AE
|
241.26
|
1
|
241.26
|
10.13
|
0.0035
|
|
BC
|
171.48
|
1
|
171.48
|
7.20
|
0.0119
|
|
BD
|
249.56
|
1
|
249.56
|
10.49
|
0.0030
|
|
BE
|
0.64
|
1
|
0.64
|
0.03
|
0.8705
|
|
CD
|
140.20
|
1
|
140.19
|
5.89
|
0.0216
|
|
CE
|
16.36
|
1
|
16.36
|
0.68
|
0.4138
|
|
DE
|
41.29
|
1
|
41.29
|
1.74
|
0.1981
|
|
Residual
|
690.00
|
29
|
23.79
|
|
|
|
Lack of Fit
|
689.62
|
22
|
31.35
|
0.83
|
< 0.0001
|
Not significant
|
Pure Error
|
0.38
|
7
|
0.05
|
|
|
|
Cor Total
|
8899.80
|
49
|
|
|
|
|
Table 5b: Analysis of variances (ANOVA) and lack-of- fit test for response surface quadratic model for removal of Pb (II) ion from aqueous solution by the chitosan
Source
|
Sum of squares
|
DF
|
Mean square
|
F-Value
|
Prob.>F
|
Comment
|
Model
|
6852.60
|
20
|
342.63
|
14.01
|
< 0.0001
|
Significant
|
A
|
1886.46
|
1
|
1886.46
|
77.15
|
< 0.0001
|
|
B
|
2421.29
|
1
|
2421.29
|
99.02
|
< 0.0001
|
|
C
|
4.21
|
1
|
4.21
|
0.17
|
0.6811
|
|
D
|
112.49
|
1
|
112.49
|
4.60
|
0.0405
|
|
E
|
5.69
|
1
|
5.69
|
0.23
|
0.6331
|
|
A2
|
47.09
|
1
|
47.09
|
1.93
|
0.1758
|
|
B2
|
1086.72
|
1
|
1086.72
|
44.44
|
< 0.0001
|
|
C2
|
14.38
|
1
|
14.38
|
0.59
|
0.4493
|
|
D2
|
141.19
|
1
|
141.19
|
5.77
|
0.0229
|
|
E2
|
43.55
|
1
|
43.55
|
1.78
|
0.1924
|
|
AB
|
280.59
|
1
|
280.59
|
11.48
|
0.0020
|
|
AC
|
28.23
|
1
|
28.23
|
1.15
|
0.2915
|
|
AD
|
41.19
|
1
|
41.19
|
1.68
|
0.2045
|
|
AE
|
182.87
|
1
|
182.87
|
7.48
|
0.0105
|
|
BC
|
119.46
|
1
|
119.45
|
4.88
|
0.0351
|
|
BD
|
126.59
|
1
|
126.59
|
5.18
|
0.0305
|
|
BE
|
117.75
|
1
|
117.75
|
4.82
|
0.0364
|
|
CD
|
95.44
|
1
|
95.44
|
3.90
|
0.0578
|
|
CE
|
0.02
|
1
|
0.01
|
8.59
|
0.9927
|
|
DE
|
46.63
|
1
|
46.63
|
1.90
|
0.1778
|
|
Residual
|
709.13
|
29
|
24.45
|
|
|
|
Lack of Fit
|
690.19
|
22
|
31.37
|
0.60
|
0.0014
|
Not significant
|
Pure Error
|
18.93
|
7
|
2.70
|
|
|
|
Cor Total
|
7561.73
|
49
|
|
|
|
|
3.3. Statistical Analysis
The fitness of the model was investigated using the analysis of variance (ANOVA) at 95% confidence interval. The ANOVA of Pb2+ removal efficiency by modified chitosan and chitosan are presented in Table 5a and 5b respectively. The significance and insignificance of each term in the model were determined by the Fisher’s F-test and P-value. The model F-value of 17.25 and 14.01 respectively for modified chitosan and chitosan, which implies that the model is significant and the P-value is less than 0.05[23], [44]. A high value of the adjusted determination coefficient (R2-Adj = 0.84 and 0.87 for chitosan and modified chitosan respectively) was estimated. This result means that 84% and 87% of the total variation on Pb(II) adsorption data can be described by the selected model. Based on the P-values presented in Table 5a, the significant model terms were A, B, C, D, E,B2, D2, E2, AB, BC, BD, BE and CD with AC, AD, CE, DE, A2, C2 and E2 insignificant to the response. In order to simplify the model Chitosan, the insignificant terms (AC, AD, CE, DE, A2, C2 and E2) can be eliminated. And based on the P-values presented in Table 5b, the significant model terms were A, B, C, D, E, A2, B2, C2, D2, E2, AB, BC, BD, BE and CD with AC, AD, CE and DE insignificant to the response. In order to simplify the model for modified chitosan, the insignificant terms (AC, AD, CE and DE) can be eliminated. Adequate precision (AP) is basically a measure of signal to noise ratio. Ratios greater than 4 indicate that the model is adequate and can be used to navigate the design space[23], [44]. In this study, the AP ratio of 19.39 and 19.90 respectively of chitosan and modified chitosan, indicate an adequate signal and thus the model can be used to predict the responses. The coefficient of variation (CV) is the ratio of the standard deviation of the mean expressed as a percentage. For a model to be considered reliable and reproducible, it must have a CV less than 10% [23]. In this investigation, the CV value of 8.34% and 7.78% respectively of chitosan and modified chitosan reflect a good precision and reliability of the experiments.
3.4. Effects of individual variables and their interactions
It can be inferred from Tables 2, 5a and 5b that the individual effects of factors (contact time, pH, adsorbent dose and initial concentration) on lead uptake by chitosan and modified chitosan were more dominant than the effect of temperature, based on their high F-values of 0.23 and 0.16 (initial concentration), 0.17 and 9.60 (adsorbent dose), 99.09 and 123.08 (pH), 77.15 and 69.01 (contact time) and 4.60 and 0.02 (temperature) respectively of the prepared adsorbents. The two factor interaction effects of initial concentration and pH (F-value of 3.36 and 12.28), initial concentration and contact time (40.70 and 5.64) respectively of the prepared adsorbent are more pronounced in the interaction. From these results, it can be concluded that the adsorption of lead from aqueous solution by chitosan and phosphoric acid modified chitosan is highly dependent on initial concentration of adsorbate and the combined effects of the contact time or pH and initial concentration of adsorbate, due to their very high F-values. Figure 7 to 9 depicts the three-dimensional response surfaces of the interaction effects of the process variables on the adsorption of lead and can be noticed that an increase in the removal efficiency of Pb (II) by prepared chitosan and the modified chitosan can be observed with decrease in initial concentration, increase in the mass of adsorbent, in the contact time and increase in pH to a maximum of about 10. The interaction effect of initial concentration and pH can be observed to show a lopsided effect on Lead removal efficiency.
3.5. Adsorption Isotherm Studies
Adsorption isotherms are useful in determining the nature of the interaction between the adsorbate and the adsorbent. To determine the most befitting isotherm model for the sorption of lead, the equilibrium data obtained using optimized values for the studied parameters were fitted to the Langmuir, Freundlich, Temkins, Dubinin-Radushkevich and Lineweaver-Burk isotherm models. The results of the isotherm parameters from the plots are presented in Table 6, and Freundlich isotherm model (Fig. 10) with coefficient of determination (R2 = 0.9286 and 0.9323 for chitosan and modified chitosan respectively) and Lineweaver-Burk isotherm model (Fig. 11) with coefficient of determination (R2 = 0.9518 and 0.9664 respectively for chitosan and modified chitosan) appeared to be much more relevant in fitting the experimental data than the other models studied.
The fitness of the Freundlich model implies that the adsorption of Pb2+ ions from bulk solution assumes heterogeneous surface energies. It can be observed from Table 6 that the n values gotten from the Freundlich plot were greater than unity for the studied concentration range; this suggests that the adsorption conditions were favourable. All the RL (dimensionless separation factor) values from the Langmuir plot lie between 0 and 1 indicating that the adsorption process is favourable for the under studied conditions [45]
Table 6: Adsorption isotherm parameters for different models studied
Isotherm Models
|
Modified chitosan
|
Chitosan
|
Langmuir model
|
|
|
qm (mg/g)
|
2.600
|
0.2413
|
KL
|
0.0374
|
0.0248
|
R2
|
0.7801
|
0.6660
|
RL
|
0.3484
|
0.4462
|
Freundlich model
|
|
|
N
|
1.718
|
1.701
|
Kf
|
0.166
|
0.154
|
R2
|
0.9323
|
0.9286
|
Temkin model
|
|
|
(Lmg-1)
|
0.423
|
0.370
|
B
|
0.496
|
0.407
|
R2
|
0.8021
|
0.7969
|
Dubinin-Radushkevich model
|
|
|
(mg/g)
|
1.224
|
1.153
|
E (KJmol-1)
|
176776.7
|
176776.7
|
(mol2KJ-2)10-6
|
4 10-6
|
4 10-6
|
R2
|
0.7909
|
0.7791
|
Lineweaver-Burk
|
|
|
R2
|
0.9664
|
0.9518
|
qm (mg/g)
|
2.600
|
0.2413
|
3.6. Adsorption Kinetic Studies
Four linearized forms of kinetic models viz; pseudo-first order, pseudo-second order, Elovich kinetic model, Bhattacharya and Venkobachar kinetic model and Weber and Morris intra-particle diffusion kinetic models have been used to analyze the collected experimental data and the linear plots obtained for the different kinetic models are presented in Fig. 15to20 with the kinetic parameters and coefficients of determination values obtained summarized in Table 7. The pseudo-second-order kinetics model fitted well to the adsorption data as observed from the high R2 value (R2 > 0.99). More so, it best predicted the value for qe than those of first-order model given in Table 7.Therefore, it can be stated that the rate-controlling step is chemisorption, involving valence forces through sharing or exchange of electrons between the adsorbent surface and adsorbate ions with no involvement of a mass transfer in solution[46]. A deviation of plot from origin was seen in the intra-particle diffusion model indicating that it is not the sole rate-limiting step of the reaction. This deviation from the origin could be because of difference in the rate of mass transfer in the initial and final step of the adsorption process [47]. The C (mg g− 1) values of the intra-particle diffusion model indicate the boundary layer thickness of Pb2+ on the adsorbent surface. The C values from the parameters were observed to be smaller than the experimental qe values (Table 7) and this means that Pb2+ uptake on these adsorbents involved both surface adsorption and partitioning inside adsorbent components such organic matter and aliphatic amino groups, among other things.
Table 7: Summary of parameters for kinetic models studied
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)
3.5. Process Optimization/Validation
The function of desirability with preparation conditions was applied. Based on economical perspective of production process, the variables were set within the studied range and goals were targeted to achieve maximum possible removal percentage by the software. The optimum condition chosen by the software was: Contact time, pH, Adsorbent dose, Temperature and Adsorbate initial concentration of 150min, 10, 2.0g, 55°C, and 50mg/L respectively to obtain highest removal efficiency. At this optimum condition, removal percentage obtained by chitosan and the modified chitosan biosorbents were 84 and 87% respectively. The experimental values obtained were in good agreement with the predicted results by the software with relatively small errors of 0.38 and 2.83% respectively. A plot of studentized residuals versus run order was tested and the residuals were scattered randomly around ± 3sigma (Fig. 21). This was an indication of better fit for both the models with the experimental data and shows that the model fits well to optimize the independent variables for the removal of Pb(II)[44].