RSM-CCD design of volcanic ash/ rice husk ash based geopolymer for crystal violet adsorption: kinetics and isotherms

doi:10.21203/rs.3.rs-4924703/v1

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RSM-CCD design of volcanic ash/ rice husk ash based geopolymer for crystal violet adsorption: kinetics and isotherms

https://doi.org/10.21203/rs.3.rs-4924703/v1

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In this work, the application of central composite design (CCD) was used for optimization of the synthesis of voclcanic ash/ rice husk ash based geopolymer in acid media. The effect of three factors such as volcanic ash mass, rice husk ash mass and phosphoric acid concentration were investigated on the responses of methylene blue index and iodine index. At an optimized conditions of voclanic ash 3.72g, rice husk ash mass 1.97g and phosphoric acid concentration 5M, the model predicted and experimental values for both responses were highly comparable. The optimized geopolymer and their precursors were characterized by XRF, FTIR and XRD. The effect of various parameters such as initial pH, adsorbent dose, contact time and initial concentration on the adsorption of crystal violet dye on optimized geopolymer was studied. The kinetic study showed that de model of pseudo-second order could very well describe adsorption kinetics. The adsorption isotherm showed that retention of crystal violet dye follows the Langmuir model.

Physical sciences/Chemistry

Physical sciences/Materials science

Geopolymer

Crystal violet

Adsorption

Central composite design

Isotherms

Geopolymers are amorphous inorganic polymers produced by alkalination of aluminosilicate materials or reaction with phosphoric acid under ambient conditions [1]. A plethora of aluminosilicate rich materials have been evaluated for geopolymer development. Naturally occurring minerals of geological origin, such as volcanic ash [2, 3], pozzolan [4], red mud [5] and laterite [6] are excellent candidates for geopolymer production. Industrial wastes such as solid waste incinerator fly ash [7], blast furnace slag [8], and fly ash [9] are also potential precursors for geopolymer synthesis. Geopolymers are an emerging class of inexpensive low-carbon adsorbents for water treatment [10]. An excellent adsorbent ought to be recyclable, environmentally friendly, affordable, chemically stable and possess high surface area (REACH). Geopolymers are particularly excellent adsorbers for dye removal from water.

For instance, bentonite derived geopolymer was synthesized for effective sequestration of crystal violet dye with a maximum adsorption capacity of 4.34 mg/g [11]. Elsewhere, Sidjou et al.[12] prepared volcanic scoria based geopolymers for the removal of CV from water and reported a maximum adsorption capacity of 11.06 mg/g. However, acidic functional groups have been reported to diminish CV uptake on an adsorbing material [13]. Since geopolymer structures possess acidic sites, strategies to optimize the surface chemistry of the geopolymers are required to maximize the adsorption of CV. Geopolymer properties depend on the synthesis conditions such as, curing temperature [14], type of activator [6], sodium silicate to sodium hydroxide ratio [7] and type and fraction of adjuvant used such as tyre ash [15], metal ions [16], charcoal powder [4], cetyltrimethylammonium bromide (CTAB) [17], metakaolin [18], chitosan [19] and hydrogen peroxide [20], among others. Modification of geopolymer porosity structures, textural properties and surface chemistry through the addition of additives provides possibilities for amelioration of the capability to remove CV by provision of energetically favorable adsorption sites, screening of unfavorable adsorption sites and improved porosity that reduces tortuosity favoring CV adsorption. For instance, the use of charcoal powder as an adjuvant was reported to increase the adsorption capacity of pozzolan based geopolymers for CV adsorption both in single and binary solute systems [4]. In a separate study, the incorporation of 1.0% CS into a metakaolin based geopolymer resulted increased the porosity structure of the composite with concomitant increase in CV adsorption efficiency of about 95% [19]. The cost and performance of the geopolymer composite depends on the type of adjuvant and the synthesis conditions. While ashes and biochars may stupendously improve the adsorptive characteristics of a geopolymer composite, their preparation may be energy-intensive increasing the cost of production. Furthermore, their chemical compositions vary widely which may alter the surface chemistry of the geopolymer composite in an unpredictable manner. Hydrogen peroxide has been shown to ameliorate the textural properties of a geopolymer without alteration of the textural properties [20]. However, the interaction of hydrogen peroxide as an additive with other synthesis variables, such as concentration of alkali activator, curing time, precursor to activator ratios has not been documented and is poorly understood.

Evaluation of such interactions may be cumbersome. Response surface methodology (RSM) provides a simple but statistically powerful tool for optimization of both the adsorbent properties from a set of process conditions [12] and the maximum adsorption efficiency from the environmental conditions [21]. The objective of this work was to optimize the porosity structures and adsorption efficiency of geopolymers using synthesis conditions and environmental factors for effective removal of CV dye from water.

2.1. Materials

The volcanic ash (VA) was obtained in the Littoral-Cameroon region, Moungo department, commune of Melong, more precisely in the Mbouroukou locality with geographic coordinates 5◦ 03′ 25″ north, 9°53′ 32″ east. The rice husk ash (RHA) was obtained by burning the rice husk at 600°C for 60 min. Rice husk was collected from a local rice mill, in the North West region of Cameroon. The CV dye, molecular formula C₂₅H₃₀N₃Cl (molecular weight = 407.99, λmax = 590 nm), was purchased from Kermel Chemicals (Cameroon).

2.2. Preparation of the geopolymer

The acid activated volcanic ash/rice husk ash based geopolymer (Gpa) was synthetised according to Tome et al [3] : Various mass of volcanic ash (VA) and rice husk ash (RHA) was mixed with phosphoric acid (solid/liquid ratio = 0.45) at various concentration determined by the design of experiment (Table 1). The slurry obtained was then cured at 80°C for 24 h. After curing for 4 days, the geopolymer obtained was immersed in acetone for 24 h to stop the polycondensation reaction, dried at 80°C, ground and then sieved to 100 µm. Finally, the samples were washed in distilled water to remove the excess acid.

2.3. CCD of experiments

Central composite design for response surface methodology was used to determine the effect of 3 factors (mass of VA, mass of RHA and concentration of phosphoric acid) on the iodine and methylene blue index during the synthesis of geopolymer. The range and levels of the experimental parameters are depicted in Table 1.

Table 1

Independent variables and their coded levels for CCD of geopolymer synthesis
Variables		Unit	Coded variables levels
			-1	0	+ 1
VA mass	X₁	g	2	3.5	5
RHA mass	X₂	g	0	1.5	3
Phosphoric acid concentration	X₃	M	5	7.5	10

Iodine uptake (response 1) and methylene blue uptake (response 2) for the geopolymer synthetised were determined using equations (1) and (2) respectively

$$\:\text{I}\text{i}=\:\frac{(B-S)}{\text{B}}\times\:\frac{MV}{m}\times\:\text{253,81}$$

$$\:\text{M}\text{B}\text{i}=\:\frac{({\text{C}}_{\text{i}}-{\text{C}}_{\text{r}})\times\:\text{V}}{\text{m}}$$

Where Ii and MBi are iodine and methylene blue uptake (mg/g) respectively, B and S are the volumes of thiosulphate solution required for blank and sample titrations, respectively. m (g) is the mass of the adsorbent, M is the concentration (mol) of the iodine solute, Ci and Cr (mg/L) are the initial and concentration at equilibrium of the methylene blue, respectively and V (L) is the volume of the solution.

A second-order polynomial response surface model for the fitting of experiment data can be describes using Eq. (3) [22] :

$$\:Y={b}_{0}+\:\sum\:_{i=1}^{k}{b}_{i}{X}_{i}\sum\:_{i=1}^{k}{b}_{ii}^{2}+\sum\:_{i=1}^{k-1}\sum\:_{j=i+1}^{k}{b}_{ij}{X}_{i}{X}_{j}$$

Where, Y = expected responses, b₀, bi, bij and bii were the regression coefficients for intercept, linear, interaction and quadratic terms respectively, whereas xi and xj were the independent factors.

2.4. Characterization of the adsorbents

The number of fonctionals groups on the geopolymer surface was studied using a Fourier Transformed Infrared (FTIR) spectrophotometer (Spectrum Gx, Germany) in the range of 4000 to 400 cm^− 1. The mineralogical phase characterization was carrieed out by quantitative X-ray diffraction. The point zero charge of adsorbents was measured by pH-drift method detailled by Gomes et al. [23].

2.5. Sorption procedure for equilibrium and kinetics studies

The sorption capacity was determined by contacting 30 mL crystal violet solution of various concentrations (10–100 mg/L) with 0.2 g of GPa on 150 mL erlenmeyer flasks at pH = 6 and temperature = 25°C for 60 min to ensure equilibrium is reached. Residual concentration of crystal violet was determined using UV-Vis spectrophotometer at 590 nm. To determine the effect of adsorbent dose on the removal of crystal violet, different masses of GPa (0.1–0.6 g) were put in contact with 30 mL of crystal violet solution (Concentration = 60 mg/L, pH = 6 and temperature = 25°C) at equilibrium time. The effect of initial pH was determined by varying crystal violet solution pH in the range 2–12 by adding 0.1 M HCl/NaOH. The amount of crystal violet sorbed onto GPa, Qe (mg/g) and the percentage removal of crystal was calculated using equations (4) and (5) respectively:

$$\:{Q}_{e}=\frac{{(C}_{0}-{C}_{e})V}{m}$$

$$\:\%R=\frac{({C}_{0}-{C}_{e})}{{C}_{0}}$$

Where C₀ and C_e (mg/L) are, respectively, the initial and equilibrium liquid-phase concentrations of crystal violet; V the volume of crystal violet solution (L); and m the weight of GPa (g).

3.1. Design of experiment using CCD

CCD was applied to develop a polynomial regression equation to analyze the relationship between the amounts of Iodine and methylene blue adsorbed during GPa synthesis, with the results presented in Table 2. The amount of iodine adsorbed ranged from 254.51 to 704.23 mg/g, and the amount of methylene blue from 14.01 to 22.28 mg/g.

Table 2

Geopolymers synthesis using CCD matrix
Runs	X₁ (g)	X₂ (g)	X₃ (M)	Ii (mg/g)	MBi (mg/g)
1	-1	-1	-1	456.85	17.88
2	1	-1	-1	437.82	16.12
3	-1	1	-1	432.99	14.13
4	1	1	-1	423,35	19.36
5	-1	-1	1	215.60	14.21
6	1	-1	1	247.46	20.27
7	-1	1	1	214.51	14.02
8	1	1	1	275.88	20.54
9	-1	0	0	404.57	14.21
10	1	0	0	456.85	20.25
11	0	-1	0	455.11	20.15
12	0	1	0	552.46	22.28
13	0	0	-1	601.22	21.68
14	0	0	1	704.23	21.35
15	0	0	0	625.13	20.79
16	0	0	0	620.11	20.96
17	0	0	0	620.11	21.56
18	0	0	0	620.11	20.94
19	0	0	0	620.11	20.96
20	0	0	0	620.11	20.95

Based on the experimental data, the centered composite design develops regression models to evaluate each response as a function of the process variables [12]. Model selection is based on the maximum values of R², Adj-R² and Pred-R², so the quadratic model is suggested to describe both responses. As shown in Figs. 1 and 2, the correlation coefficients (R²) between experimental and predicted data were 0.9064 (Ii) and 0.9382 (MBi); these values were close to unity. Both R² coefficients were relatively high and in reasonable agreement with the adjusted R² values of 0.8721 and 0.8826 for iodine and methylene blue removal respectively, indicating that the predicted values are in agreement with the actual values. The resulting model equations for Ii and MBi are given in equations 6 and 7 respectively.

$$\:\text{I}\text{i}\:(\text{m}\text{g}/\text{g})=622.22+{11.68\text{x}}_{1}{\:+\:8.64\text{x}}_{2}-{69.46\text{x}}_{3}+{4.86\text{x}}_{1}{\text{x}}_{2}+{{15.24\text{x}}_{1}{\text{x}}_{3}+8.21\text{x}}_{2}{\text{x}}_{3}-{192.88\text{x}}_{1}^{2}-{119.88\text{x}}_{2}^{2}{\:+\:29.14\text{x}}_{3}^{2}$$

$$\:\text{M}\text{B}\text{i}(\text{m}\text{g}/\text{g})=21.15+{2.21\text{x}}_{1}{\:+\:0.1695\text{x}}_{2}+{0.1219\text{x}}_{3}+{0.9326\text{x}}_{1}{\text{x}}_{2}+{1.14\text{x}}_{1}{\text{x}}_{3}+{0.0714\text{x}}_{2}{\text{x}}_{3}-{4.10\text{x}}_{1}^{2}-{0.1151\text{x}}_{2}^{2}{\:+\:0.1791\text{x}}_{3}^{2}$$

3.2. Analysis of variance

An analysis of variance (ANOVA) was applied to further substantiate the suitability of the models. The ANOVA of iodine index quadratic model listed in Table 3 shows that the model is significant with the F-value of 10.76 and Prob > F of 0,0005. There is therefore a 0.05% chance that variation may occur due to noise. Adequate precision (AP) measures the signal/noise ratio, with a ratio greater than 4 being desirable [24]. In this case, the ratio of 10.80 for the iodine index indicates a very suitable signal, which means that this model can be used to navigate the design space. Prob > F values below 0.05 indicate the significance of the model terms [25]. In this case, as shown in Table 3, only x₃, x₁² and x₂² are the significant terms.

Still in Table 3, the analysis results for the methylene blue index response during GPa synthesis show that the signal/noise ratio (adequate precision) of 12.21 indicates an adequate signal. The low predicted R² value implies that several non-significant effects have been included in the model. Thus, the main effect of volcanic ash mass (x₁) and its quadratic effect (x₁²) are significant for the model response. As for the interactions, x₁x₂ and x₁x₃ are significant. An adequate precision of 12.21 also indicated an adequate signal, allowing us to deduce that this model can also be used to navigate in the design space.

Table 3

ANOVA for the response surface model for both responses.
	Ii					MBi
Source	DF	Sum of squares	F-value	Prob > F	Source	DF	Sum of squares	F-value	Prob > F
Model	9	44327.08	10,76	0,0005	Model	9	16.58	16.88	< 0.0001
x₁	1	1364.96	0,3313	0,5776	x₁	1	48.74	49.62	< 0.0001
x₂	1	745.67	0,1810	0,6796	x₂	1	0.2872	0.2924	0.6005
x₃	1	48240.30	11,71	0,0065	x₃	1	0.1486	0.1513	0.7055
x₁x₂	1	189.12	0,0459	0,8347	x₁x₂	1	6.96	7.08	0.0238
x₁x₃	1	1857.65	0,4508	0,5171	x₁x₃	1	10.37	10.56	0.0087
x₂x₃	1	538.78	0,1308	0,7252	x₂x₃	1	0.0408	0.0416	0.8425
x₁²	1	1.023E + 05	24,83	0,0006	x₁²	1	46.27	47.11	< 0.0001
x₂²	1	39472.47	9,58	0,0113	x₂²	1	0.0364	0.0371	0.8511
x₃²	1	2334.71	0,5666	0,4690	x₃²	1	0.0882	0.0898	0.7706
R² = 0.9064 Adj-R² = 0.8721 Pred-R² = 0.2720 Adequate precision = 10.8064					R² = 0.9382 Adj-R² = 0.8826 Pred-R² = 0.0200 Adequate precision = 12.2123

3.3. 3D response surface plots

Table 3 clearly shows that phosphoric acid concentration has the strongest effect on the iodine index due to the highest F-value of 11.71, while volcanic ash and rice husk ash masses and had weak effects on this response with F-values of 0.33 and 0.18 respectively. The effects of the interactions are all inconsistent. Nevertheless, the effect of the interaction between volcanic ash mass and phosphoric acid concentration is higher than the other two, with an F-value of 0.45.

The 3D response surface plot of the interaction between volcanic ash mass and phosphoric acid concentration shown in Fig. 3 shows that the iodine index is highest when the volcanic ash mass is at the ash end of the range and the phosphoric acid concentration decreases.

For the methylene blue index response, we can see that the effect of volcanic ash mass is more significant with an F value of 49.62, while the other two effects, namely rice husk ash mass and phosphoric acid concentration, are similar and inconsistent (Table 3). The effect of the volcanic ash mass/phosphoric acid concentration interaction is greater than that of the volcanic ash/rice husk ash mass interaction, with F values of 10.56 and 7.08 respectively. On the other hand, the effect of the interaction rice husk ash mass/phosphoric acid concentration is weak and inconsistent with an F-value of 0.04.

The 3D plot representing the variation of the methylene blue index response with the interaction of volcanic ash mass/rice husk ash mass (Fig. 4) shows that this response is maximal in the center of the domain. However, we note that an increase in the mass of volcanic ash and the mass of rice husk also has a positive effect on the methylene blue index response. For the volcanic ash mass/phosphoric acid concentration interaction at a fixed rice husk ash mass (x₂ = 1.5 g) shown in Fig. 5, we observe that an increase in methylene blue index is due to a simultaneous increase in volcanic ash mass and phosphoric acid concentration. Indeed, the formation of geopolymers from volcanic ash and phosphoric acid can increase the specific surface area and adsorption capacity of the resulting materials.

3.4. Responses optimization

When optimizing GPa synthesis, the aim was to maximize iodine and methylene blue indices. By exploiting the response surfaces, optimal conditions during GPa synthesis were obtained for volcanic ash mass = 3.72 g, rice husk ash mass = 1.97 g and phosphoric acid concentration = 5 M with predicted responses of 704.23 mg/g and 21.33 mg/g respectively for iodine and methylene blue indices with a desirability of 0.84. However, the experimental values obtained under the same conditions for iodine index and methylene blue index were 703.88 and 21.82 mg/g respectively (see Table 4).

Table 4

GPa synthesis parameters optimization
	Variables				Iodine index (mg/g)			Methylene blue index (mg/g)			Desirability
	X₁(g)	X₂ (g)	X₃ (M)		Exp	Pred	residual	Exp	Pred	residual
GPa	3.72	1.97		5.00	703.88	704.23	0.35	21.82	21.33	0.49	0.94

3.6. Feedstocks and geopolymer characterization

The chemical composition of volcanic ash and rice husk ash determined by XRF analysis is given in Table 5. For volcanic ash, SiO₂, Al₂O₃, Fe₂O₃ and CaO are the main oxides with 41.52, 15.90, 14.74 and 9.67 wt.%, respectively. SiO₂/Al₂O₃ ratio (2.61) and SiO₂ + Al₂O₃ sum (57.42) in VA present this material as those used for geopolymer synthesis with their basic ingredient values contained within the ranges reported in the literature [26–29]. It was observed that after heat treatment, RHAs contain mainly SiO₂ (80.20 wt.%) with a significant amount of K₂O (4.95 wt.%).

Table 5

Chemical composition of VA and RHA.
Oxides	SiO₂	Al₂O₃	Fe₂O₃	CaO	MgO	TiO₂	Na₂O	K₂O	P₂O₅	LOI
VA	41,52	15,90	14,74	9,67	8,29	3,45	2,30	0,63	0,75	2,44
RHA	80,20	1,54	0,48	0,57	3,22	0,09	0,15	4,95	3,03	5,63

The FTIR spectra of rice husk ash, volcanic ash and geopolymer are shown in Fig. 6. The spectrum of RHA (Fig. 6a) shows bands around 873, 793 and 466 cm^− 1 which are respectively attributed to the symmetrical stretching vibration of Si-OH and/or Al-OH, Si-O or Al-O and to the bending vibration of Si-O-Si. The band at 1091 cm^− 1 is attributed to asymmetric stretching of the SiO₄ unit. For VA and GPa, the absorption bands at 1007 and 1091 cm^− 1 are attributed to the asymmetric stretching vibrations of SiO₄ units of various connections (Si-O-Si, Si-O-Al and Si-O-Fe) [30, 31]. The characteristic bonds of the O-H group and water molecules are located at 3428 cm^− 1, 1646 and 1612 cm^− 1 [32, 33]. The peaks appearing at 572 cm^− 1 correspond to the vibration of the Si-O bond [33, 34] and the peak at 466 cm^− 1 represents the vibration of the Si-O-Fe bond [3, 35].

The XRD spectra of rice husk ash, volcanic ash and geopolymer are shown in Fig. 7. The mineralogical composition of RHA (Fig. 7a.) includes calcite, CaCO₃ (PDF# 86-2343), cristobalite, SiO₂ (PDF#87-2096), diopside sodian, (Ca_0.52Na_0.29Fe_0.10Mg_0.09) (Mg_0.057 Fe_0.14Al_0.27Mn_0.01Ti_0.01) (Si₂O₆), (PDF#85–1692), albite Na(AlSi₃O₈), (PDF#71-1156) and anorthoclase, Na_0.75K_0.25(AlSi₃O₈) (PDF#89–1459). In VA's DRX spectrum (Fig. 7b.), we find : albite disordered Na(AlSi₃O₈), (PDF#20–0572), Forsterite ferrous Mg_1.641Fe_0,359SiO₄, (PDF# 88-1993) dolomite CaMg(CO3)₂, (PDF#75-1759), anorthoclase, Na_0.75K_0.25(AlSi₃O₈) (PDF#89–1459), cristobalite SiO₂, (PDF#76–0939), anorthite sodian disordered, (Ca, Na) (Si, Al)₄O₈, (PDF# 10–0360), Diopside manganian, Ca_0.87Mn_0.19Mg_0.94Si₂O₆), (PDF#83-1834) and Labradorite Ca_0.65Na_0.32(Al_1.62Si_2.38O₈), (PDF# 83-1367). Comparing the diffractograms of volcanic ash to that of optimized geopolymer (GPa), we can observe a decrease in the intensity of certain peaks, evidence of the dissolution of minerals during the geopolymerization process [36]. We also note the presence of a dome between 20° and 40°, which suggest that the rice husk ash contributed amorphous silica during geopolymerization.

3.7. Study of operating parameters for CV dyes adsorption by GPa

3.7.1. Effect of initial pH solution

This experiment was carried out at a pH between 2 and 12, a contact time of 60 min, an initial CV concentration of 60 mg/l, an adsorbent dose of 0.2 g and a temperature of 25°C. The results shown in Fig. 8. indicate that the adsorption rate of CV increases with increasing solution pH. When the solution pH is below the pHpzc (5.4) of the geopolymer material (GPa), protonation of the active binding sites was enhanced by the sharp increase in proton (H+), so the number of active sites became lower for sorption of CV molecules. On the other hand, at pH above pHpzc, the surface acquires an increasingly negative charge from 6 onwards, increasing the adsorption of cationic CV dye molecules due to electrostatic interaction [37–40].

3.7.2 Effect of adsorbent dose

To assess the influence of adsorbent dose on the percentage adsorption of CV by GPa geopolymer, given quantities of GPa in the range (0.1–0.6 g) were dispersed in a series of experiments containing 30 ml for an initial CV concentration of 60 mg/l. The solution was then stirred at room temperature at pH 6 for 60 minutes. The effect of adsorbent dose on crystal violet removal is shown in Fig. 9. From this figure, we can see that increasing the adsorbent mass leads to an increase in the adsorption rate. This can be attributed to the increase in surface area available and the increase in the number of active sites on the surface of the adsorbent used [12, 37]. On the other hand, the decrease in adsorption rate observed at 0.5 g may be due to agglomeration and overlapping of adsorption sites, resulting in a reduction in the total number of sites accessible to the pollutant [41–43].

3.7.3. Effect of contact time and initial concentration

Thermodynamic equilibrium between the adsorbate in the liquid phase and the adsorbate bound to the solid is achieved at a rate that depends not only on the rate at which the constituents in the adsorbent and in the fluid, but also on the interaction between the adsorbent/adsorbate interaction. Studying the adsorption of a compound on an adsorbent enables us to examine the influence of contact time and initial concentration on retention. Figure 10. shows the effect of contact time and initial concentration on the adsorption of crystal violet by GPa. From this figure, we can see that the crystal violet has a good affinity for GPa. The shape of the curves shown is typical of saturation curves, with a slight qualitative and quantitative difference. We can see that CV adsorption took place very rapidly from the start of the experiment, and equilibrium was reached very quickly after 60 minutes. Beyond that, there was almost no further increase in adsorption. These results confirm that CV adsorption on the GPa surface is a spontaneous speed phenomenon in the initial moments, slowing down after 60 minutes in a state of adsorption saturation. This phenomenon can therefore be divided into two stages: a rapid initial phase due to the availability of the more frequently active sites that were vacant and spontaneously accessible to the CV particles, followed by a slower phase in which the remaining unoccupied surface sites diminish due to the repulsive forces between the solute and the solid phases [44, 45].

With regard to the influence of initial concentration, we note that the adsorption capacity of crystal violet increases with initial concentration. This may be due to the increasing mass gradient, which acts as a driving force to overcome the mass transfer resistance of pollutants. Nevertheless, the increase in initial concentration can lead to the appearance of a plateau indicating saturation of the support, which can be explained by the exhaustion of all the active sites existing on the surface of the support [46, 47].

3.8. Adsorption kinetics

The design of adsorption treatment systems requires knowledge of kinetic processes due to the diversity of chemical systems, the nature of the different adsorbents and the different designs of contact systems. For this reason, two liquid-phase adsorption kinetic models, the pseudo-first-order model [48] and the pseudo-second-order model [49], were used in this study to analyze experimental adsorption kinetic data.

Lagergren's first-order equation is one of the most widely used for the sorption of a solute from a liquid solution [50] and is represented as follows :

$$\:\text{ln}\left({q}_{e}-{q}_{t}\right)=ln{q}_{e}-{k}_{1}t$$

Where q_e is the quantity of dye adsorbed at equilibrium (mg/g), q_t is the quantity of dye adsorbed at time t (mg/g), K₁ is the first-order reaction rate constant (L/min).

The expression of the pseudo-second-order model is in the form quoted by Ho and Mckay [49] :

$$\:\frac{t}{{q}_{t}}=\frac{1}{{k}_{2}{q}_{e}^{2}}+\frac{1}{{q}_{e}}t$$

Where k₂ (g/mg/min) are the constants of pseudo-second-order model.

The curves for the two models are shown in Fig. 11. and the constants obtained from the different models are summarized in Table 6.

Table 6

Pseudo-first-order and pseudo-second-order adsorption rate constants for the different initial CV concentrations
		Concentration (mg/L)
Kinetic models	Parameters	20	40	60	80	100
	q_e (mg/g)	3.93	6.37	8.64	12.68	17.14
Pseudo-first order	q_emax (mg/g)	0.319	0.461	0.705	0.759	0.487
	K₁ (min^− 1)	0.039	0.042	0.046	0.032	0.032
	R²	0,960	0.981	0.990	0.991	0.966
Pseudo-second order	q_ecal (mg/g)	3.949	6.399	8.684	12.729	17.170
	K₂ (g/mg.min)	0.379	0.263	0.187	0.131	0.214
	h (mg/g.min)	5.912	10.785	14.106	21.173	63.091
	R²	0.999	0.999	0.999	0.999	0.999

The R² values shown in Table 6 suggest that the pseudo-second-order model best describes the CV adsorption process on GPa. We also note that the adsorbed quantities calculated by this model are closer to those determined experimentally. This suggesting that chemisorption might be rate-determining step for controlling the adsorption process [51–53].

3.9. Adsorption isotherms

The adsorption isotherm is a simple tool, but it plays a very important role in understanding adsorption mechanisms and finding the best adsorbent for large-scale applications. These isotherms provide information on adsorbent/adsorbate affinity and an idea of the binding energy between adsorbate and adsorbent. Crystal violet adsorption per GPa was studied as a function of initial dye concentration. The results obtained were modeled using the two empirical models.

The Freundlich model uses the exponential distribution of adsorption sites and energies in an adsorption process, assuming that the sites on the adsorbent surface are heterogeneously distributed, and is represented by the following equation [54]:

$$\:{q}_{e}={k}_{F}{C}_{e}^{\frac{1}{n}}$$

Where, k_F (L/g) and n are Freundlich constants for adsorption capacity and adsorption intensity respectively.

Langmuir's isothermal model (Eq. (11)) assumes a homogeneous adsorbent surface and monolayer adsorption.

$$\:{q}_{e}=\frac{{q}_{m}{K}_{L}{C}_{e}}{1+{K}_{L}{C}_{e}}$$

Where: qm is monolayer adsorption capacity (mg/g), K_L is the Langmuir isotherm constant related to the affinity of the binding sites and energy of adsorption (L/mg).

The estimated model parameters with the correlation coefficient (R²) for the different models are presented in Table 7. The R² values are taken as a measure of the goodness of fit of the experimental data to the isotherm models. The applicability of the two isotherm models to current data follows the following order: Langmuir > Freundlich. The essential characteristics of the Langmuir isotherm can be expressed in terms of the dimensionless separation parameter R_L, which indicates the shape of the isotherm that predicts whether an adsorption system is favorable or unfavorable [55]. R_L is defined as follows [56] :

$$\:{R}_{L}=\frac{1}{1+{R}_{L}{C}_{0}}$$

The R_L value for the current experimental data lies between zero and one, indicating favorable adsorption of crystal violet on GPa.

Table 7

The Langmuir and Freundlich isotherms model constants.
		Langmuir isotherm						Freundlich isotherm
Adsorbants	K_L		q_emax	R_L	RMSD	R²	K_F		n	RMSD	R²
GPa	0.235		14.668	0.040	0.498	0.996	4.187		2.117	0.166	0.987

The preparation conditions for a geopolymer based on volcanic ash and rice husk ash (i.e. volcanic ash mass, rice husk ash mass and phosphoric acid concentration) were optimized using a three-factor central composite design. The responses studied were iodine index and methylene blue index. Under optimized preparation conditions (volcanic ash mass 3.72g, rice husk ash mass 1.97g and phosphoric acid concentration 5.0M), the experimental values for iodine number and methylene blue number were 703.88 mg/g and 21.82 mg/g respectively. The adsorbent was characterized and used for crystal violet adsorption in water. Experimental data on the kinetics of the adsorption process confirm the pseudo-second-order kinetic model, suggesting that the rate of adsorption depends on the availability of adsorption sites. The adsorption process was further investigated using Langmuir and Freundlich adsorption models. The equilibrium data fit the Langmuir isotherm model better, suggesting a homogeneous adsorption process to describe the interaction between the dye and the geopolymer obtained under optimal conditions. The present study concludes that GPa could be used as a low-cost adsorbent and for CV removal in aqueous solution.

Author contributions

All authors contributed to the review conception, writing sections of the original manuscript, and reviewing of manuscript. All authors read and approved the final manuscript.

Data availability

The data will be provided upon request to the corresponding author of this article.

Conflicts of interest

The authors have no relevant financial or non-financial interests to disclose. Acknowledgements

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript

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RSM-CCD design of volcanic ash/ rice husk ash based geopolymer for crystal violet adsorption: kinetics and isotherms

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1. Materials

2.2. Preparation of the geopolymer

2.3. CCD of experiments

2.4. Characterization of the adsorbents

2.5. Sorption procedure for equilibrium and kinetics studies

3. Results and discussion

3.1. Design of experiment using CCD

3.2. Analysis of variance

3.3. 3D response surface plots

3.4. Responses optimization

3.6. Feedstocks and geopolymer characterization

3.7. Study of operating parameters for CV dyes adsorption by GPa

3.7.1. Effect of initial pH solution

3.7.2 Effect of adsorbent dose

3.7.3. Effect of contact time and initial concentration

3.8. Adsorption kinetics

3.9. Adsorption isotherms

Conclusion

Declarations

References

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