3.1 Biosorption: kinetics and equilibrium study
Biosorption of AR14 onto W. anomalus biomass as a function of contact time, at four initial dye concentrations (50, 100, 150, and 200 mg L− 1) are depicted in Fig. 1. The result indicates that for all tested concentrations, the rate of biosorption increased with increasing contact time up to 10 min, after that, an equilibrium was reached. Indeed, three phases were recorded in the biosorption process of AR14 into the cell wall. At the beginning, a rapid biosorption rate occurred, thereafter the dye molecules biosorbed gradually until the maximum biosorption capacity of biosorbents was reached. The initial phase (5 min) can be attributed to the availability of large surface area and many vacant macropores for the dye uptake. Also, the dye biosorption at this phase was rapid, suggesting the involvement of a passive process like physical adsorption or ion exchange interaction on the cell surface. The second stage (5 to 10 min), was characterized by gradual and slow uptake. This result can be explained by the saturation of active functions groups on the cell surface [26]. As well as, by the repulsive forces among adsorbed dye molecules and those present in the solution [27]. At the third stage, the biosorption process reached equilibrium state. Similar findings were reported previously on the biosorption of Strazone blue [28], Acid blue 161 [29], and Direct red 23 [30] using S. cerevisiae biomass.
As shown in the Fig. 2B, the pseudo-first-order model was not applicable to the AR14-yeast biosorption system, there was no linearization of experimental data, and the R2 of all the studied concentrations were less than 90% (Table 2) [31]. On the other hand, the R2 value of the pseudo-second-order model was relatively close to 1 for all tested concentrations, suggesting therefore that this model seems to be a good fit for this biosorption system. Moreover, the values of Qe (cal) calculated were closer to the experimental values Qe (exp), confirming the goodness-of-fit of the model. The distinguished features of this model are: (i) the rate of biosorption is determined by adsorption capability rather than adsorbate concentration, (ii) it is well for the complete data range of contact time, and (iii) is in agreement with chemisorption being the rate-limiting step [32]. However, the pseudo-second order model did not identify the potential mechanism of diffusion into the pores. The kinetics were then analyzed through the intraparticle diffusion model. Indeed, the Qt was plotted relative to the (√t) and are given in Fig. 2B, and the resulting kinetics parameters are given in Table 2. It has been shown that if biosorption follows an intraparticle diffusion model, the plot of (Qt) versus (√t) should be linear, and if the plot passes through the origin (C = 0), the adsorption kinetics follows the intraparticle diffusion model. However, if (C # 0), this indicates some degree of boundary layer control. The intraparticle diffusion model is not the rate control step of the biosorption process, but it can work simultaneously with other diffusion models [33]. As shown in the Fig. 2B, there are two linear regions representing the diffusion boundary layer, followed by intraparticle diffusion in macro pore [34]. This result demonstrates the occurrence of intraparticle diffusion and indicates that the yeast biomass reaches biosorption equilibrium when diffusion occurs in the macro pore layer, and the resistance to mass transfer is not involved in the dye uptake kinetics [35]. Kismir et Aroguz [36] reported that the process of mass transfer of dye onto the biosorbents can take place in general through four steps: (i) bulk diffusion (transfer from bulk solution to the surface of the biosorbent), (ii) film diffusion (transfer through the boundary layer to the biosorbent surface), (iii) intra-particle diffusion (transfer from the surface to the interior pores of the particle), (iv) chemical reaction via ion-exchange, complexation, chelation; the adsorption of dye at an active site on the biosorbent surface.
Table 2
Kinetic parameters of AR14 biosorption on yeast biomass
Models
|
kinetics parameters
|
Initial dye concentrations (mg L− 1)
|
50
|
100
|
150
|
200
|
Pseudo first order
|
K1 ( min− 1 )
|
0.205
|
0.194
|
0.215
|
0.195
|
R2
|
0.857
|
0.477
|
0.609
|
0.524
|
Pseudo second order
|
K2 (g mg− 1 min− 1)
|
0.126
|
0.051
|
0.022
|
0.014
|
R2
|
0.999
|
0.999
|
0.999
|
0.998
|
Qe (cal)
|
23.980
|
46.510
|
69.930
|
95.230
|
Qe (exp)
|
24.038
|
50.000
|
67.129
|
92.082
|
Intra-particle diffusion
|
Ki1
|
7.847
|
14.66
|
21.65
|
29.190
|
C1
|
0.549
|
1.277
|
0.955
|
0.179
|
R12
|
0.983
|
0.974
|
0.993
|
0.999
|
Ki2
|
0.030
|
0.083
|
0.022
|
0.028
|
C2
|
23.59
|
45.657
|
68.835
|
93.527
|
R22
|
0.920
|
0.254
|
0.148
|
0.035
|
According to the graphical representation of Langmuir and Freundlich isotherms (Fig. 3), and the corresponding models’ constants listed in the Table 3, the R2 was respectively at 0.954 and 0.849. We may conclude that Langmuir isotherms describe an appropriate fit to the biosorption of AR14 onto W. anomalus compared to Freundlich isotherms. Langmuir isotherms predict that the biosorption is based on monolayer adsorption on a homogeneous site without any interaction between biosorbed dye molecules on neighboring sites, it also assumes that there is equal energy for all sites, and that there are multiple biosorption sites for a specific surface, and when these sites are fully saturated, no further biosorption can take place [26, 37]. Aksu et Dönmez [31] reported the applicability of both Langmuir and Freundlich models to the biosorption system of Remazol Blue reactive dye into dried cells of Candida strains.
Table 3
Biosorption isotherm models and their corresponding parameters for the biosorption AR14 by W. anomalus biomass
Langmuir
|
Freundlich
|
Qmax ( mg g-1)
|
KL (L mg-1)
|
R2
|
1/n
|
Kf
|
R2
|
126.58
|
0.107
|
0.954
|
0.5
|
15.87
|
0.849
|
3.2 Characterization of yeast biomass
3.2.1 SEM coupled with EDX analysis
SEM and EDX analysis were used to establish the changes in morphology and the elemental composition of the yeast biomass before AR14 biosorption, with a view to establish the mechanism of dye binding to the cell wall. The Fig. 4A shows the SEM images of colorless yeast cells, and the Fig. 4B shows W. anomalus after AR14 biosorption in 8000× magnification. There were notable differences in cellular morphology between the control and AR14-loaded cells. The yeast cells prior to AR14 biosorption had a normal shape and a transparent outer layer outside the cell surface. However, after biosorption of the dye molecules, the cell becomes smooth and presents hazy textures. Furthermore, the X-ray spectra showed a difference in the intensity of elementary peaks of C, O, P, and K after dye biosorption.
3.2.2 FTIR spectroscopy analysis
The FTIR spectra (Fig. 5) of W. anomalus biomass before and after AR14 biosorption were used to identify the involved functional groups of the cell wall in the dye-biosorption process [38]. The strong vibration around 3000–3500 cm− 1 indicates the existence of stretching vibration of hydroxyl (-OH) and/or amino (-NH2) groups, which are among the functional groups of chitosan and amino acids on the cell wall of W. anomalus [39]. The peaks at 2854–2923 cm− 1 are in the region of the absorption of lipid acyl chains (3050 − 2800 cm− 1) that correspond to symmetric and asymmetric stretching of methylene and methyl groups in the membrane phospholipids [40]. The strong peaks at 1642 cm− 1 detected in the region between 1700 and 1500 cm− 1 indicate the presence of amide (I and II) bands, mainly from protein peptide bonds (C = O stretching and N-H bending), corresponding to several uronic acids and amino acids present in the cell wall [41]. The infrared absorption in the spectral at 1406 cm− 1 may be caused by the sulfur (-SO-) and phosphorous (-PO-) groups, which range between 1500 and 1300 cm− 1 corresponding to vibrations of fatty acids and proteins [39]. The bands observed at 1073 cm− 1 are assigned to C–O stretching vibration of alcohols and carboxylic acids which are mainly related to complex vibrations from carbohydrates [42]. Furthermore, after AR14-biosorption, a significant change was noticed in the transmittance of these functional groups which may be attributed to its occupation by dye molecule. Based on the comparison of the spectra before and after AR14 biosorption, there were also a shift in various bands, which represented the groups involved in the biosorption corresponding to hydroxyl, carboxylic acid, amine and amino groups. Some previous studies indicated also that the biosorption process is accomplished by chelation and formation of ionic bridges between dye molecules and functional groups. The binding mechanism of dye molecules to the yeast cell is explained by the strong attractive forces of peptidomannan, peptidoglycan, lipids and heteropolysaccharides…, which contains several functional groups such as carboxyl, hydroxyl, amino, phosphate, and other charged groups [43, 44]. In line with our findings, it has been reported by Dilarri et al. [29] that the biosorption of synthetic dyes by the cell wall of S. cerevisiae involved some amide and amine groups, and demonstrated that the vibration of (–C = O) and (–C–O) can be another linking group in the chitin structure. Moreover, it can be predicted according to the FTIR analysis that Yoshida H-binding, dipole–dipole H-binding, π–π and n–π interactions play an indispensable role in the AR14 biosorption onto yeast cell [45]. The cell wall architecture and the proposed mechanisms of AR14 biosorption are illustrated in Fig. 6. In fact, the major components of the S. cerevisiae cell wall are β-glucans (formed by 1,3-β- and 1,6-linkages) and mannoproteins (proteins highly N- or O-glycosylated mannose residues linked by 1,2,-1,3-, 1,4- and 1,6-α-linkages), which represent about 50–60% and 40–50%, respectively of the cell wall mass, as well as 1–3% for the chitin, which is manufactured by 1,4-β-N-acetylglucosamine [46]. These complex macromolecular structures present potential binding sites for diverse pollutants including toxic dye molecules [47].
3.2.3 Zeta potential and Zero-point charge measurement
The electrical state of the cell surface is one of the critical parameters in biosorption studies. Thus, the measurement of zeta potential can be one of the key parameters related to the external loads of the adsorbent [48]. As shown in Fig. 7A, the zeta potential of W. anomalus biomass was maintained at a negative charge, regardless of the initial pH value, and it varied from − 4.6 mV at pH 3 to -9.28 at pH 10. This can testify the anionic characteristics and the high concentration of acid functional groups on the surface of W. anomalus. These results are consistent with previous studies that evaluated the zeta potential of different yeast strains [49, 50]. It is known that there was a close relationship between the zeta potential of biomaterials and their biosorption capacity [51]. On the other hand, the value of pHzc at which ΔpH = 0 was found at 5.25 (Fig. 7B) confirmed thus the presence of anionic groups on the cell surface that dominate over the cationic groups. Zehra et al. [18] reported that the acidic value of pHzc of baker’s yeast, was attributed to the presence of various biopolymers on the yeast cell wall, mainly β-glucan and chitosan, that may have caused a substantial load on the external surface of the cells and given a net charge on the surface that depending on the pH caused by deprotonation of functional groups in the cell wall [52, 53]. The number of positively charged sites decreases, when the pH of the solution increases. The decrease in the biosorption of AR14 anions is a result of the electrostatic repulsion forces of the negatively charged cell surface at acidic pH condition [54].
3.2.4 Contact angle measurements
Based on both Vogler’s and Van Oss approaches, W. anomalus exhibited a hydrophilic character, the θw value (34.9 ° ± 0.4) was less than 65 °, and the ΔGiwi had a positive value (37.23 ± 1.13 mJ m− 2). These findings agree with previous results reporting the hydrophilic character of various yeast strain [55, 56]. In fact, yeast surface hydrophobicity has been shown to be related to the proteins of cell surface [57, 58]. In addition, W. anomalus appear to behave predominantly as electron donors/Lewis bases with high values of γ− = 52.57 ± 0.6 mJ m− 2 (Table 4). These results also indicate that this strain exhibit weak electron acceptor characters with γ+ = 0.24 ± 0.07 mJ m− 2. Some previous research showed also that microbial cell surfaces are mainly electron donating, while electron-accepting cell surfaces are rarely found [59], which may be due to the presence of phosphate groups in cell wall [57]. Theses finding are consistent with the FTIR analyses of W. anomalus, that confirm the presence of phosphate groups on its cell surface. On the other hand, the θw of W. anomalus significantly decreased from 34.9 º ± 0.4 to 31.6 º ± 0.9 after dye biosorption (Table 4). This may be attributed to an increase in the density of polar functional groups on the biomass surfaces after the biosorption process [60]. In addition, a significant variation was noted in the electron donor and acceptor character after dye biosorption (γ+ from 0.24 ± 0.07 to 0.08 ± 0.02 mJ m− 2; and γ− from 52 57 ± 0.6 to 59.79 ± 0.5 mJ m− 2) which can be attributed to the interaction of cell surface functional groups with dye molecules.
Table 4
Contact angle values using water (θw), formamide (θF) and diiodomethane (θD), Lifshitz-vander Waals (γ LW), electron-donor (γ−) and electron-acceptor (γ+) parameters, surface energies (ΔGiwi) of W. anomalus cells before and after AR14 biosorption
|
Contact angles (°)
|
Surface tension: components and parameters (mJ m− 2)
|
Surface energies
|
θw
|
θF
|
θD
|
γLW
|
γ+
|
γ−
|
ΔGiwi (mJ m− 2)
|
Raw Biomass
|
34.9 ± 0.4
|
42.2 ± 0.6
|
49.5 ± 1.6
|
34.45 ± 0.9
|
0.24 ± 0.07
|
52.57 ± 0.6
|
37.23 ± 1.13
|
AR14-Biomass
|
31.6 ± 0.9*
|
44.1 ± 0.5ns
|
36.3 ± 0.6***
|
41.33 ± 0.3***
|
0.08 ± 0.02*
|
59.79 ± 0.5***
|
44.9 ± 1.2**
|
*statistical significance p value < 0.05 |
3.3 Optimization of the biosorption by using the design of experiments
3.3.1 Factor’s screening using Plackett–Burman design
Based on the positive or negative values of the factors modeled on Eq. 10, the factors A and C have positive values, indicating, therefore, their synergistic effect to the biosorption response, while the other negative factors indicate an antagonistic effect.
D % = 11.0 + 0.933 A − 3.64 B + 12.89 C − 0.003 D − 0.207 E + 0.774 F Eq. (10).
Table 5 represents the value of the regression coefficients, t-tests, and p-values for the six independent variables. The multiple correlation coefficient R2 of the first-order model indicates that 75.82% of the data variance could be estimated by the model. Yet, the difference between the predicted R2 (51.80%) and adjusted R2 (67.28%) indicated that it is impossible to evaluate the relationship between the significant independent variables and the response based on the first-order model. As illustrated in Fig. 8A, the biomass dosage (89.06%), pH (26.57%), and the dye concentration (10.94%) are significant influence factors on biosorption capacity. Indeed, the increase in the yeast dosage showed a positive influence, while increasing dye concentration and pH had a negative influence on the efficiency of AR14 biosorption (Fig. 8B). Several studies reported that an increase in biomass dosage significantly improves the biosorption capacity, due to the increase of exchangeable sites present in the cell wall [24]. Evidently, pH is also one of the factors controlling the biosorption process. In fact it can affect the chemistry of dye molecules, the physicochemical properties of the yeast surface and the magnitude of electrostatic charges imparted by the ionized dye molecules [61]. It has been documented that the optimal biosorption pH of particular dyes depends closely on their chemical properties. For instance, the biosorption of reactive dyes requires an acidic conditions, whereas basic dyes requires neutral or alkaline conditions [28]. In addition, the net charge of the biosorbent is also pH dependent, at a low pH value, the functional group on the cell surface becomes protonated and acquires a net positive charge increasing thereby the binding of anionic dyes. Regarding the effect of the initial dye concentration on the biosorption capacity, its closely depending on the binding sites available on the biomass surface. Moreover, it has been reported that a high dye solute uptake can be obtained at a high initial dye concentration, which is linked to the high driving force for mass transfer [62, 63]. However, at lower initial dye concentrations, the biosorption becomes independent of the initial dye concentration, because the ratio of the initial moles of the solute to the available area becomes low [64].
Table 5
ANOVA for process variables of the PBD
Source
|
DF
|
Adj SS
|
Adj MS
|
F-Value
|
P-Value
|
Model
|
6
|
10216.7
|
1702.79
|
8.88***
|
0.000
|
Linear
|
6
|
10216.7
|
1702.79
|
8.88***
|
0.000
|
Temperature (ºC) (A)
|
1
|
751.4
|
751.39
|
3.92NS
|
0.064
|
pH (B)
|
1
|
1272.1
|
1272.06
|
6.64**
|
0.02
|
Biomass dosage (g.L− 1)) (C)
|
1
|
2241.6
|
2241.58
|
11.69**
|
0.003
|
Agitation (rpm) (D)
|
1
|
0.8
|
0.82
|
0.00 NS
|
0.949
|
Dye (mg L− 1) (E)
|
1
|
5821.6
|
5821.58
|
30.37***
|
0.000
|
Contact time (min) (F)
|
1
|
129.3
|
129.30
|
0.67 NS
|
0.423
|
Error
|
17
|
3258.8
|
191.69
|
|
|
Lack-of-Fit
|
5
|
1860.8
|
372.16
|
3.19*
|
0.046
|
Pure Error
|
12
|
1398.0
|
116.50
|
|
|
Total
|
23
|
13475.5
|
|
|
|
R2 75.82; Adjusted R2 67.28; Predicted R2 51.80%. |
DF: degrees of freedom; SS: sum of squares; MS: mean sum of squares, |
*Significance (α = 0.05) |
3.3.2 Optimization of process parameters using Box-Behnken model
The Eq. (11) model the relationship between the predicted biosorption response and the process parameters with the second-order polynomial equation. In fact, the negative values of both factors (A) and (C) indicates that a high biosorption efficiency occurs at lower dye concentration and at acidic pH solution.
D% = 70.8–0.243 A + 48.99 B – 23.18 C – 0.00015 A*A -10.50 B*B + 1.625 C*C + 0.0001 A*B + 0.0419 A*C – 2.61 B*C Eq. (11)
As illustrated in the Table 6, the regression of the quadratic model was statistically significant (F = 21.48; P = 0.00), at 95% confidence limits. The R2 indicates that the model as fitted explains 90.62% of the variability, suggesting a significant correlation between the predicted and experimental effects of the selected variables. Furthermore, the predicted R2 value (76.27%) was in line with the founded R2value (86.41%) of the adjusted, indicating that 10.14% of the variability in the response was not explainable by this model. Also, the F value was significantly proportional to the pure error, and the F values for the factors A, B and C were at 17.44, 42.69, and 79.62, respectively, showing consequently that the pH of the solution was most significantly different from zero at the 95.0% confidence level followed by the biomass dosage and dye concentration (Fig. 9A). Consistent with the results of the factor selection step, the normalized effects indicate that increasing the yeast dose had a positive influence, whereas a negative influence on biosorption efficiency was observed when increasing the dye concentration or initial pH of the medium (Fig. 9B). The contour plots displaying the variation of AR14-biosorption, with respect to the simultaneous shift in two factors, indicates the relationship and the interaction impact of two variables with varying concentrations on the biosorption, while the third variable is apprehended at the central point. As described in the Fig. 10A, when the initial pH was maintained at 5, the biosorption rate was proportional to the increase in the biomass dosage, but inversely proportional to the increase in the initial dye concentration. Meanwhile, when the initial dye concentration was maintained at 125 mg L− 1 (Fig. 10B), the rate of biosorption was proportional to the increase in the biomass dosage, and the optimum biosorption was achieved at a lower pH level. Similarly, when the biomass dosage was maintained at 1.125 mg L− 1 (Fig. 10C), the optimum biosorption level was proportional to the initial pH of the medium and the initial dye concentration. Higher biosorption was achieved at acidic conditions with lower dye concentration. El-Naggar et al. [65] reported that the dye biosorption system was depends on protonation or deprotonation of various functional groups on the cell surface. At pH 3–4, the protonation of amino groups on the yeast cell wall increases the net positive charge and enhances the biosorption of negatively charged dye ions by electrostatic binding.
Table 6
Analysis of variance for response surface quadratic model.
Source
|
DF
|
Adj SS
|
Adj MS
|
F-Value
|
P-Value
|
Model
|
9
|
3821.96
|
424.66
|
21.48***
|
0.000
|
Linear
|
3
|
2762.70
|
920.90
|
46.58***
|
0.000
|
Dye (mg L− 1)(A)
|
1
|
344.76
|
344.76
|
17.44***
|
0.000
|
Biomass (g L− 1) (B)
|
1
|
843.87
|
843.87
|
42.69***
|
0.000
|
pH (C)
|
1
|
1574.06
|
1574.06
|
79.62***
|
0.000
|
Square
|
3
|
620.61
|
206.87
|
10.46***
|
0.000
|
A * A
|
1
|
3.03
|
3.03
|
0.15NS
|
0.700
|
B * B
|
1
|
257.68
|
257.68
|
13.03**
|
0.002
|
C * C
|
1
|
312.17
|
312.17
|
15.79***
|
0.001
|
2-Way Interaction
|
3
|
438.65
|
146.22
|
7.40**
|
0.002
|
A * B
|
1
|
0.00
|
0.00
|
0.00 NS
|
0.997
|
A * C
|
1
|
315.76
|
315.76
|
15.97***
|
0.001
|
B * C
|
1
|
122.89
|
122.89
|
6.22**
|
0.022
|
Error
|
20
|
395.38
|
19.77
|
|
|
Lack-of-Fit
|
3
|
361.66
|
120.55
|
60.77***
|
0.000
|
Pure Error
|
17
|
33.72
|
1.98
|
|
|
Total
|
29
|
4217.34
|
|
|
|
R2 90,62; Adjusted R2 86,41; Predicted R2 76,27%. |