Characterizations of the synthetic Hydroxyapatite
XR D analysis
The structure of Klin-HA materials was investigated using XRD analysis to detect distinct phases present in the starting powders as well as recognized the chemical composition (El-Nahas, Safaa et al., 2020). Three XRD patterns were showes in Fig. 2 (a,b) for raw cement bypass dust cement and the four prepared hydroxyapatite samples (Kiln-HA), respectively.The data indicated that calcite CaCO3 was the most prevalent component percentage in bypass dust (39.1%), followed by Larnite Ca2SiO4 (25.2%) and Al2O3 (14.4%), as shown in Fig. 2 (a). Other small components such as SiO2, Periclase MgO, Fe2O3, and CaO were also present, as shown in Table 2, Others identified comparable components in Egyptian cement kiln dust (Salem, W. M. et al., 2015). Furthermore, the XRD pattern revealed that all synthesised hydroxyapatite samples had hexagonal crystal structures, which corresponded to card number COD 9011095 for the pure phase of hydroxyapatite Fig. 2(b) and Table 1. The major diffraction peaks of Kiln-HA were appeared at 2θ = 25.8°, 31.4°, 49.4° and 53.4°(Khawar et al., 2019; Maleki et al., 2019). The crystallite size of the hydroxyapatites studied ranged from 14.85 to 25.72 nm, placing them in the Nano-scale, according to previous research (Abo-El-Enein, 2017).
Table 1
Chemical structure, phases, and the Crystallite size for Hydroxyapatite samples
Sample
name
|
major
phase
|
Chemical structure
|
JCPDS card
|
Crystallite size
nm
|
Kiln-HA 1
|
Hydroxyapatite
|
Ca5HO13P3
|
COD 9011092
|
16.43
|
Kiln-HA 2
|
Hydroxyapatite
|
Ca5HO13P3
|
COD 9011095
|
25.72
|
Kiln-HA 3
|
Hydroxyapatite
|
Ca5HO13P3
|
COD 9011095
|
14.85
|
Kiln-HA 4
|
Hydroxyapatite
|
Ca5HO13P3
|
COD 9011095
|
17.12
|
Table 2
Chemical composition of Cement by-pass dust
Card
|
Parameter
|
% Content
|
COD 9015835, 9016022
|
CaCO3 Calcite
|
39.1%
|
COD 1537314
|
K1.5 Na0.5 S
|
11.4%
|
COD 9006307
|
SiO2
|
5.9%
|
COD 9012790
|
Ca2O4Si Larnite
|
25.2%
|
COD 1200005
|
Al2O3
|
14.4%
|
COD 1528612
|
Fe2O3
|
1.8%
|
COD 1000053
|
MgO Periclase
|
1.1%
|
COD 1011094
|
CaO
|
1.1%
|
SEM and EDX analyses
The morphological features of four synthetically Kiln-HA samples are shown in Fig. 3 (a-d). In the case of Kiln-HA1, Kiln-HA2, and Kiln-HA3 samples, the (SEM) photos seemed to be in the shape of a star or coral reefs, while Kiln-HA4 was sheet-like in appearance.Other hydroxyapatite materials have been observed to have a similar morphology (Barakat et al., 2008; Maleki et al., 2019). According to the EDX analysis, the examined materials mostly include P, Ca, O, and H, with trace levels of additional elements including Mg, Fe and Ti Fig. 4 and Table 3show the percentages of elements in the Kiln-HA compound. The obtained ratios matched the XRD patterns and were in good accord with the projected value of 1.67 (Iconaru et al., 2018) .
Table 3
EDX analysis for Hydroxyapatite samples (Kiln-HA1 to Kiln-HA4
samples
|
Elemental Analysis %
|
Ca/P
|
|
Na
|
Mg
|
Al
|
Si
|
P
|
K
|
Ca
|
Ti
|
Fe
|
Kiln-HA 1
|
7.40
|
1.00
|
2.47
|
1.04
|
31.63
|
**
|
52.90
|
**
|
2.85
|
1.67
|
Kiln-HA 2
|
8.51
|
0.78
|
2.74
|
**
|
32.99
|
0.21
|
51.37
|
0.15
|
3.24
|
1.70
|
Kiln-HA 3
|
4.02
|
1.01
|
2.61
|
0.54
|
34.22
|
0.42
|
54.26
|
0.17
|
2.76
|
1.59
|
Kiln-HA 4
|
0.42
|
0.90
|
2.39
|
**
|
36.23
|
0.27
|
56.87
|
**
|
2.93
|
1.57
|
FTIR analysis
Photospectroscopy analysis was employed to validate the functional groups of Kiln-HA.The FT-IR spectrum of the synthesized Kiln-HA nanostructure was seen in Fig. 5. The appeared peak around 3600–3400 cm-1 was attributed to the stretching Oــــ H.While, peaks displayed around 1600–1500 cm-1was recognized to the Oــــ Hbending of the surface adsorbed water.The phosphate bands (PO4-3) for a specific type of apatite were located at 1061-1007cm-1,574–533cm-1 and 634–607cm-1(Garskaite et al., 2014; Iconaru et al., 2018). The carbonate (CO32-) functional group wasappearedat wavenumbers 1405.91cm-1 wich was indicatedby thevibration C– O band of (CO32-) group. Hydroxyapatite may include some tiny CaO compounds that have the capacity to bind CO2 and form calcium carbonate CaCO3 molecules appeared in FTIR analyses (Garskaite et al., 2014; Popa Cristina Liana et al., 2016) .
Surface area measurements
SBET methods were used to estimate porosity, specific surface area, pore volume, and pore size (El-Nahas, Safaa et al., 2020). The four evaluated hydroxyapatite samples revealed high specific surface area in the order: Kiln-HA3 (161.5 m2/g) > Kiln-HA1 (130.2 m2/g) > Kiln-HA2 (81.9 m2/g) > Kiln-HA4 (54.1 m2/g) as displayed in Fig. 6 (a, b) and Table 4. The hydroxyapatite samples under investigation have a greater specific surface area than previously published materials (Abo-El-Enein, 2017). At P/P0, the adsorbed amount reached a limiting value, indicating a type I isotherm with a concave P/P0 axis. The classic I-type denotes the presence of microporous materials (Huang et al., 2019).
Table 4
Surface properties for fabricated hydroxyapatite samples
Sample name
|
BET square
(m2.g− 1)
|
Total pore volume
(cm3.g− 1)
|
Aaverage pore radius
(Aº)
|
Kiln-HA1
|
130.18
|
0.06779
|
10.4
|
Kiln-HA2
|
81.96
|
0.04111
|
10.03
|
Kiln -HA3
|
161.49
|
0.08302
|
10.28
|
Kiln -HA4
|
54.149
|
0.02891
|
10.3
|
Thermo-gravimetric analysis
As demonstrated by the TG curves in the heating diagram was in Fig. 7 one stage for the Kiln-HA1 and Kiln-HA2 samples, but was in two stages for the Kiln-HA3 and Kiln-HA4 samples. The total weight losses for tested samples were in the first stage were 10–12%, indicating that all of the samples were thermally stable. The first step of sample weight loss occurred when the temperature was below 100°C, which was ascribed to the release of adsorbed water in the samples. Furthermore, at temperatures below 115°C, water in the channels evaporates, resulting in a one-step approach. The second stage of weight loss appeared solely in Kiln-HA3 and Kiln-HA4 samples around temperature 250°C. Decomposition of nitrates, acetates, or ammonia in the starting materials may occur at temperatures between 250 and 350°C (Grigoraviciute-Puroniene et al., 2019; Mondal et al., 2016). DTG curves verified the information provided from TG curves. Kiln-HA1 and Kiln-HA2 samples displayed a single DTG peak at 75 oC and 97 oC, respectively. While, Kiln-HA3 and Kiln-HA4 samples demonstrated double DTG peaks at (84oC, 249 oC) and (95 oC, 252 oC), respectively. Higher temperatures can produce dehydroxylation in apatite samples, which has been recorded at temperatures beyond 700°C (Garskaite et al., 2014).
The DSC curves revealed that all the peaks below 100 oC in apatite samples showed endothermic nature owing to evaporated adsorbed water. All hydroxyapatite samples exhibit a substantial wide endothermic peak at 218, 285, 280, and 268oC which are attributable to coordinated water in the lattice. Sharp endothermic peaks at temperatures above 700 oC were observed due to the decomposition of hydroxyapatite and formation of tri-calcium phosphate (Sava et al., 2015). or formation of Ca2P2O7 (Mousa et al., 2013).
Study of Point of Zero Charge (PZC)
For determination of the acidity or basicity of the adsorbent's surface, measurements of the surface pH for tested adsorbent samples were performed (El- Nahas, et al.,2017). Figure 8 depicted the pHZ of all hydroxyapatite samples, showing that the observed value was around 7.2. The surface will be a positively charged at pH < 7.2 and will be negatively charged with pH > 7.2. A similar approach was observed in other studies (Hokkanen et al., 2018). Cationic species are predicted to commence adsorption at pH values more than 7.2, whereas anionic compounds will be adsorbed at pH levels greater than 7.2 (Ananth et al., 2018; Hokkanen et al., 2018).
Effect of initial pH
One of the most essential topics in terms of practicality was determining the correct pH for optimising the elimination process (Venkatesan et al., 2019). The surface charge of an adsorbent and the degree of ionisation of metals are both affected by the pH of a solution (El Kady et al., 2016). With raising the pH solution, the elimination of Fe (III) and Mn (II) increased dramatically, as seen in Fig. 9(a, b). According to point of zero charge measurements in section (3.2), the cationic metals will be efficiently removed at pH 7.2.The concentration of hydrogen ion (H+) is large at low pH values, resulting in competition with metal ions over adsorbent sites The Fe (III) and Mn (II) metal ions started to precipitate at pH levels greater than 8. Because effective removals began at pH = 7.2, this was chosen as the optimal pH for for continued adsorption research. Others followed suit (El Kady et al., 2016).
Effect Metal Initial Concentrations
The adsorption mechanism and the number of adsorbent active sites used are both affected by the initial metal ion concentration (Garskaite et al.,2014 ). Studying the effect of iron and manganese ions concentration on the removal process were employed in the range of 100–500 mg.L-1 and the results were domnestrated in Fig. 10 (a,b) According to the findings, when the iron and manganese levels were increased, the removal performance was lowered. As the initial metal ion concentrations increases, the sorption sites become saturated The existence of a finite number of active sites at a particular dosage of adsorbent might explain the phenomena. Other researchers had the same conclusion (El-Nahas, Safaa et al., 2020).
Effect of Contact Time
It's crucial to know how long water treatment systems take to reach their peak performance. The influence of duration was investigated in this study throughout time intervals ranging from 1 minute to 120 minutes. Initially, the amount of metal ion adsorbed rises quickly until reaching saturation at equilibrium. The metal ions are promptly adsorbed on these sites because there are more adsorption sites accessible at initially, resulting in a rapid spike in the sorption process (Abo-El-Enein, 2017; El Kady et al., 2016). Fe ions were eliminated at a rate of more than 98% in less than one minute, and equilibrium was reached after 20 minutes. In the case of Mn ions, 80% clearance was accomplished in 10 minutes, despite the equilibrium takes longer (120 minutes). The results were illustrated in Fig. 11(a, b). The sluggish adsorption process was related to pore diffusion, once after saturation, both surface and pore binding sites would be occupied, preventing further adsorption (Huang et al., 2019).
Effect of adsorbent dosage
The adsorbent dose is an essential property since the overall surface area has a direct impact on the removal process (Mohandes Salavati-Niasari, 2014). The sorption capacity of Fe and Mn ions were enhanced via increasing amount of adsorbent material. This might be related to the fact that the number of active sites on the surface of adsorbent materials has increased (El-Nahas, Safaa et al., 2020; El Kady et al., 2016) (Huang et al., 2019). Only a minimal dose (0.06 g/L) of Kiln -HA samples was required for more than 96 percent clearance of Fe ions, while sorption of Mn ions required more Kiln-HA samples (at least 1 g/L) to achieve 82 percent removal as illustrated in Fig. 12 (a, b).
Effect of Temperature
The sensitivity of sorption process for changing temperature from 25 to 55°C were demonstrated in Fig. 13 (a, b). The removal of Fe3+ and Mn2+ augmented significantly with increasing of the temperature. This phenomenon indicating that the adsorption process has the endothermic nature. Various published papers for other hydroxyapatites revealed the endothermic nature of certain metal ion sorption (Gupta et al., 2011) (Qian et al., 2014).
Thermodynamic parameters
The adsorption process is affected by temperature, which causes existing bonds to break and metal cation mobility to rise, leading to the development of new binding adsorption sites (El Kady et al., 2016). The evaluation of thermodynamic parameters is required in order to determine the spontaneity of the adsorption process as well as the feasibility of operation at a certain temperature. The thermodynamic parameters calculated at temperatures of 25, 35, 45, and 55°C were the change in Gibbs free energy (ΔG° in kJ/mol), enthalpy (ΔH° in kJ/mol), and entropy (ΔS° in kJ/mol K). shows the equations that were used to assess these parameters, while the results shown in Table 5 while the results were demonstrated in Tables 6 and 7 and Fig. 14. At all working temperatures, the hydroxyapatite samples exhibited negative ΔG° values, showing that the adsorption process is feasible and spontaneous. The positive values of ΔH° indicate that the removal of metal ions by apatite materials is an endothermic process. It really revealed a complex interaction between the adsorbate and adsorbents involving numerous processes (as: adsorption & ion exchange) (El Kady et al., 2016; Kim et al., 2020). More negative ΔG° values were obtained as the temperature goes up, indicating that the adsorption process is more favorable at higher temperatures (El-Nahas, Safaa et al., 2020). A positive entropy value suggests an increase in disorder. The entropy change for the system is always positive in a spontaneous process (Abo-El-Enein, 2017).
Table 5 Thermodynamic parameters
![](data:image/png;base64,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)
Table 6
Thermodynamic parameters for adsorption system of Fe ions onto Hydroxyapatite
Sample
|
ΔH°
(kJ/mol)
|
ΔS°
J/mol. K
|
Δ G° ( kJ/mol )
|
25°C
|
35°C
|
45°C
|
55°C
|
Kiln-HA 1
|
88.06
|
337.27
|
-12.45
|
-15.82
|
-19.20
|
-22.57
|
Kiln-HA 2
|
55.81
|
237.52
|
-14.98
|
-17.35
|
-19.73
|
-22.10
|
Kiln-HA 3
|
34.75
|
143.82
|
-8.11
|
-9.54
|
-10.98
|
-12.42
|
Kiln-HA 4
|
99.77
|
362.18
|
-8.16
|
-11.87
|
-15.41
|
-19.03
|
Table 7
Thermodynamic parameters for adsorption system of Mn ions onto Hydroxyapatite
Sample
|
ΔH°
(kJ/mol)
|
ΔS°
J/mol. K
|
Δ G ° ( kJ/mol )
|
25°C
|
35°C
|
45°C
|
55°C
|
Kiln-HA1
|
28.70
|
115.51
|
-5.72
|
-6.88
|
-8.03
|
-9.19
|
Kiln-HA2
|
30.82
|
120.78
|
-5.17
|
-6.38
|
-7.59
|
-8.80
|
Kiln-HA3
|
5.52
|
36.15
|
-5.25
|
-5.61
|
-5.97
|
-6.34
|
Kiln-HA4
|
12.89
|
67.93
|
-7.35
|
-8.03
|
-8.71
|
-9.39
|
Equilibrium Studies
Adsorption isotherms are mathematical models that may be used to determine which design is optimal for a certain application. Adsorption isotherms give physicochemical details on how adsorption takes place and how the interaction between the adsorbent surface and the adsorbent happens (El Kady et al., 2016). The three well-known isotherm models parameters used in this research were Langmuir, Freundlich, and Temkin illustrated in Table 8 (Ayawei et al., 2017). The maximal adsorption capacity (qmax), which describes monolayer coverage of adsorbate per (g) of adsorbent, is calculated using the Langmuir model.
The plots and values of experimental equilibrium data of Fe (III) and Mn (II) adsorbed on Kiln-HA samples showed higher linearity for all three models (Langmuir, Freundlich, and Temkin), as displayed in Fig. 15 and Tables 9 and 10. That is, all three models are capable of adequately describing the sorption process. The R2 values of all three models were also relatively comparable to one another, suggesting that Fe(III) and Mn(II) uptake by Kiln-HA samples is intricate and may be explained by several mechanisms (Moreno, et al., 2010). The maximum adsorption capabilities (qmax) were found to be between 147 and 175 mg.g− 1 for Fe (III) whereas varied from 204 to 344 mg.g− 1to Mn(II).The values of (qmax) were greater than those of other hydroxyapatite-adsorbent materials previously published (Ahmed et al., 2021). The RL value in Langmuir model ranges between 0 and 1 reflected the favorable adsorption process of Fe (III) and Mn (II) ions onto Kiln-HA (Qian et al., 2014). Furthermore, the n value for the Freundlich isotherm is larger than unity that indicates the removal of iron and manganese ions is favorable (Darweesh, 2020). Also, high values of Kf indicate high adsorption efficiency for iron and manganese ions by Kiln-HA (El Kady et al., 2016). The binding constants obtained from Temkin isotherm parameters were in range of 67–79 and 21.4–52.4(J.mol− 1) for Fe3+ and Mn2+, respectively that showing adsorbents have the higher affinity for binding with the Kiln-HA(Darweesh, 2020). Figures 16 and 17 illustrate the nonlinear patterns of three adsorption models (Langmuir, Freundlich, and Temkin) on hydroxyapatite, which support the linear models' verified findings.
Table 8 Linear and non-linear isotherms equations used in this work
![](data:image/png;base64,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)
Table 9
Langmuir, Freundlich and Temkin Isotherm constant Fe ions onto Hydroxyapatite
Sample
|
Langmuir constants
|
Freundlich constant
|
Temkin constant
|
|
qmax
(mg. g-1)
|
KL
(mg /L)
|
R2
|
RL
|
n
|
Kf
(mg/g)
|
R2
|
b
J/mol
|
B
|
A
|
R2
|
Kiln-HA 1
|
159
|
6.30
|
0.99
|
1.8*10− 3
|
2.78
|
117.9
|
0.98
|
70
|
35.4
|
58.9
|
0.93
|
Kiln-HA 2
|
175
|
6.33
|
0.99
|
1.8*10− 3
|
2.90
|
140.9
|
0.96
|
69
|
36.1
|
64.7
|
0.96
|
Kiln-HA 3
|
147
|
8.50
|
0.98
|
1.6*10− 3
|
2.94
|
121.3
|
0.91
|
79
|
31.3
|
69.6
|
0.99
|
Kiln-HA 4
|
172
|
5.27
|
0.98
|
2.6*10− 3
|
2.61
|
107.9
|
0.97
|
67
|
36.9
|
33.7
|
0.84
|
Table 10
Langmuir, Freundlich and Temkin Isotherm constant Mn ions onto Hydroxyapatite
Sample
|
Langmuir constants
|
Freundlich constant
|
Temkin constant
|
|
qmax
(mg. g-1)
|
KL
(L/mg)
|
R2
|
RL
|
n
|
Kf
(mg/g)
|
R2
|
b
J/mol
|
B
|
A
|
R2
|
Kiln-HA 1
|
204
|
0.042
|
0.99
|
0.19
|
1.94
|
18.01
|
0.98
|
52.36
|
47.32
|
0.37
|
0.99
|
Kiln-HA 2
|
344
|
0.039
|
0.99
|
0.20
|
0.88
|
3.01
|
0.94
|
21.43
|
115.6
|
0.16
|
0.94
|
Kiln-HA 3
|
243
|
0.036
|
0.99
|
0.22
|
1.57
|
11.59
|
0.92
|
38.50
|
64.34
|
0.23
|
0.99
|
Kiln-HA 4
|
232
|
0.069
|
0.99
|
0.15
|
1.63
|
21.16
|
0.99
|
44.71
|
55.42
|
0.62
|
0.96
|
Kinetic Studies
Kinetic analysis is commonly used to estimate the rate of adsorption and offers valuable insight into the mechanism and rate controlling step of adsorption process (Sultan et al., 2018). Many kinetic models, including pseudo-first-order, pseudo-second order, and intraparticle diffusion, have been employed to study the adsorption process. The linear forms of kinetic models were reported in Table 11 and their plots were shown in Figs. 18 and 19. The sorption process matched the pseudo-second-order kinetic better than the pseudo-first-order kinetic, according to R2 values as presented in Tables 12 and 13. It's worth noting that the qe experimental results are virtually identical to those predicted using pseudo-second-order kinetics. Despite the fact that pseudo-second-order kinetics does not fully reflect merely chemical adsorption (El-Nahas, et al., 2020; El Kady et al., 2016). The ion-exchange mechanism at the charged surface can also be articulated by the pseudo-second order model (Plazinski et al., 2009). According to published study, the sorption process suited the pseudo-second-order kinetics model well at lower initial concentrations, whereas the pseudo-first-order kinetics model fit better at greater initial concentrations (Azizian, 2004). Since the plots produced from the intraparticle model (Weber–Morris equation) did not pass through the origin point as illustrated in Fig. 20, the rate-adsorption-controlling step is not solely through the diffusion process (El-Nahas, et al., 2020).
Table 11 Kinetic models used in this work
![](data:image/png;base64,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)
Table 12
Kinetic Studies, pseudo-first, pseudo second and intraparticle diffusion Constant of Fe ions onto Hydroxyapatite
Sample
|
Pseudo-first order
|
Pseudo-second order
|
Intraparticle diffusion
|
qe
(Exp)
|
|
K1
|
qe
|
R2
|
K2
|
qe
|
R2
|
Kad
|
R2
|
C
|
Kiln-HA 1
|
0.013
|
0.023
|
0.992
|
0.762
|
45.0
|
0.999
|
0.002
|
0.977
|
44.96
|
44.99
|
Kiln-HA 2
|
0.008
|
0.12
|
0.992
|
1.57
|
43.59
|
0.999
|
0.015
|
0.974
|
43.47
|
43.59
|
Kiln-HA 3
|
0.072
|
0.09
|
0.899
|
0.797
|
43.90
|
0.999
|
0.088
|
0.750
|
42.92
|
43.89
|
Kiln-HA 4
|
0.062
|
0.34
|
0.881
|
0.614
|
43.07
|
0.999
|
0.032
|
0.884
|
42.73
|
43.06
|
Table 13
Kinetic Studies, pseudo-first, pseudo second and intraparticle diffusion Constant of Mn ions onto Hydroxyapatite
Sample
|
Pseudo-first order
|
Pseudo-second order
|
Intraparticle diffusion
|
qe
(Exp)
|
K1
|
qe
|
R2
|
K2
|
qe
|
R2
|
Kad
|
R2
|
C
|
Kiln-HA 1
|
0.014
|
15.81
|
0.927
|
0.003
|
47.62
|
0.999
|
0.363
|
0.810
|
34.47
|
47.25
|
Kiln-HA 2
|
0.001
|
4.80
|
0.753
|
0.069
|
44.84
|
0.999
|
0.162
|
0.876
|
42.58
|
48.50
|
Kiln-HA 3
|
0.002
|
5.18
|
0.990
|
0.022
|
44.19
|
0.999
|
0.367
|
0.982
|
39.97
|
45.75
|
Kiln-HA 4
|
0.009
|
5.27
|
0.943
|
0.019
|
46.34
|
0.999
|
0.312
|
0.90
|
42.38
|
48.0
|
Mechanism for removing of Fe and Mn ions on Kiln-HA surface
According to the findings of this study, The removal of Fe and Mn ions via Klin-HA samples was explained by two pathways; an ion exchange mechanism and an adsorption process.XRD analysis indicated the formation of substituted metal-hydroxyapatite Ca5-xMx(PO4)3(OH) as seen in Fig. 20 (a,b). The major diffraction peaks for Fe-HA detected at 2-theta were 25.7, 31.6, and 28.5 for loaded Fe2+ ions, and peak strength dropped as other diffraction peaks faded away (Huang, et al., 2019). The substituted Mn-hydroxyapatite Ca2Mn3(PO4)3(OH) were identified by XRD analysis (COD 2236693) after adsorption process As a result, the ion exchange mechanism appears to have played a role in the elimination process. Adsorption on HA surfaces is the first mechanism, followed by an ion exchange interaction between metal ions adsorbed and Ca2+ ions on HA surfaces, according to previous researchers (Moreno et al., 2010).
The electronegativity of Fe and Mn ions in aqueous solution is greater than that of the Ca ion in apatite, which might explain how hydroxyapatite samples can remove metal ions from water via the replacement process (Abdulaziz and Faid, 2013). Furthermore, Ca2+ sites have a larger ionic radius (0.99 Å), but Fe3+ (0.64 Å) and Mn2+ (0.80 Å) have a smaller ionic radius which appears to have an appreciable influence (bin Jusoh, A., et al.2005; Horta, M., et al, 2019; Sirait, M., et al, 2020). It has been demonstrated that calcium phosphate acts not only as a source of adsorption centers but also enables ion-exchange process (Moreno et al., 2010). The replacement process or ion exchange appeared according to the following. (3):
Ca5(PO4)3(OH) + X (Fe3+or Mn2+) →Ca 5-x (Fe3+orMn2+)x(PO4)3(OH) + X (Fe3+ orMn2+).…(3)
Where (x) refer to the substituted calcium ions by iron or manganese ions. The hydroxyapatite lattice is very flexible, allowing for effects and vacancies in either cationic or anionic locations (Encinas-Romero et al., 2013).
Metals removal in binary and tertiary system
In most water sources, iron and manganese ions coexist with other cations. We tested mixture of varied proportions of (Fe3+: Mn2+) solutions as (1:1), (2:1), and (1:2) with a total concentration of 100 mg/L. That explore the completion process of eliminating both iron and manganese ions from aqueous solutions via the active site of Kiln-HA samples. As illustrated in Fig. 21(a,b,c), the findings demonstrated that removing Fe (III) from aqueous solutions took priority over removing Mn (II) in all three cases studied. That can be explained as the Fe3+ions have a stronger electronegativity and a smaller ionic radius than Mn2+ which explains its selectivity (Samuelsson et al., 2007). Moreover, variations in hydration energy and hydrated ionic radii across metal ions also contributed to this selectivity (Pan et al., 2016). The competitive elimination of Fe 3+ in the mixed metal ions combination (Fe/Mn/ Cu) showed a similar trend to the binary (Fe3+/Mn2+) solution.Metal ions were eliminated in the following order: Fe3+> Cu2+> Mn2+ as dommnestrated in Table 14. For other cations, additional researcher revealed a similar tendency (Pan et al., 2016). In this research, the manufactured Kiln-HA had a greater adsorption capability to different metal ions, indicating that hydroxyapatite is a promising adsorbent to remediate real wastewater
Table 14
mixture of metal ions (Fe3+, Mn2+ and Cu2+) affect the removal efficiency
Sample name
|
Fe3+ Removal%
|
Mn2+ Removal%
|
Cu2+ Removal%
|
KilnHA1
|
99.9%
|
87.4%
|
99.5%
|
KilnHA2
|
99.8%
|
89.5%
|
99.4%
|
KilnHA3
|
99.9%
|
77.0%
|
95.4%
|
KilnHA4
|
99.8%
|
79.0%
|
97.8%
|
Continuous Reusability of Nano-hydroxyapatite
When an adsorbent can be reused numerous times with acceptable efficiency, the cost to set up an adsorption process is minimized (Khawar et al., 2019). Continuous sorbent reusability enhances system performance and lowering operational costs over time. The adsorption experiment was carried out three cycles continuously without regeneration and their results represented in Fig. 22 (a, b). Ability of Kiln-HA samples to remove Fe and Mn ions were up to 90% for both cations in the first cycle. However, after 3 cycles the adsorption ability had dropped to 20% without regeneration. It can be concluded that the synthetic adsorbents of Kiln-HA are effective and may be utilized several times with good adsorption capacity.
Comparative Study with previous studies
A comparison of different adsorbents' adsorption capabilities against synthesized hydroxyapatite were displayed in Table 15. Kiln-HA' samples had a greater maximum adsorption capacity, ranging from 147 to 175 mg/g for Fe3+ and from 204 to 344 mg/g for Mn2+.The recycling of cement bypass to useable hydroxyapatite materials exhibited superior iron and manganese ion elimination efficiency from aqueous solution than other published papers. The hydroxyapatite materials employed in this work are efficient adsorbents with a lot of potential for extracting iron and manganese ions from water at extremely high concentrations (100–1000 ppm).
Table 15
Comparative Study of tested Hydroxyapatite with published materials for removal of Fe+ 3 and Mn+ 2 ions
Materials
|
Max adsorption capacity (mg/g)
|
Initial conditions
(ppm)
|
%Removal efficiency
|
References
|
Natural Apatite
|
0.174 mmol/g for Fe3+
|
Conc. Fe 200 ppm
|
90% for Fe + 3
|
(Qian et al., 2014)
|
Magnetotactic bacteria
|
47.86 mg/g for Fe2+
15.26 mg/g for Mn2+
|
Conc. Fe 3ppm
Mn 8 ppm
|
48% Fe
15% Mn
|
(Diaz-Alarcon et al., 2019)
|
AC from Local Argo-Residues
|
10.64 mg/g for Fe3+
6.66 mg/g for Mn2+
|
Conc. Fe &
Mn 5ppm
|
12% Fe
13% Mn
|
(A. Akl, 2013)
|
Cow Bone Charcoal
|
31.43 mg/g for Fe2+
29.56 mg/g for Mn2+
|
Conc. Fe &
Mn 20 ppm
|
30% Fe &Mn
|
(Moreno et al., 2010)
|
Modified Nano-Hydroxyapatite -MgO
|
0.603 for Fe2+ mg/g
|
Conc. Fe 2 ppm
|
60% for Fe + 3
|
(Ayash et al., 2019)
|
Granular activated carbon
|
3.60 mg/g for Fe2+
2.54 mg/g for Mn2+
|
Not included
|
Not included
|
(Bin Jusoh et al., 2005)
|
Nano-hydroxyapatite
|
55.2 mmol/g for Fe2+
|
Conc. Fe 100 ppm
|
95% for Fe
|
(Abo-El-Enein S., 2017)
|
white rice husk ash
|
15.2 mg/g for Mn2+
|
Conc. Mn 20 ppm
|
15% for Mn
|
(Tavlieva et al., 2015)
|
Iron and manganese-coated pumice
|
Not included
|
Conc. Fe 15 ppm
Mn 5 ppm
|
84% Fe
72% Mn
|
(Indah et al., 2017)
|
Zeolite Y
|
31.45 mg/g for Fe2+
18.02 mg/g for Mn2+
|
Conc. Fe 1.15 ppm
Mn 1.61 ppm
|
96% Fe
83% Mn
|
(Kwakye-Awuah et al., 2019)
|
microporous chitosan/polyethylene glycol
|
71.4 for Fe2+
18.5 mg/g for Mn2+
|
Conc. Fe &
Mn 2 ppm
|
Not included
|
(Reiad et al., 2012)
|
superparamagnetic Nano-hydroxyapatite
|
0.54 mg/g for Fe2+
0.61 mg/g for Mn2+
|
Conc. Fe & Mn 50 ppm
|
85% Fe &Mn
|
(El Kady et al., 2016)
|
Removal of Iron and Manganese by precipitation
|
Not included
|
Conc. Fe 0.1 ppm
Mn 0.05 ppm
|
92% Fe
96% Mn
|
(Salem, M. et al., 2012)
|
Apatite IITM
|
126 mg/g for Fe2+
124 mg/g for Mn2+
|
Conc. Fe &Mn 75 ppm
|
98% Fe
96% Mn
|
(Oliva et al., 2010)
|
Modified Hydroxyapatite
|
0.389 mg/g for Fe3+
|
Conc. Fe 2 ppm
|
50% for Fe3+
|
(El nsar et al., 2017)
|
Nano hydroxyapatite
Kiln-HA1
Kiln-HA2
Kiln-HA3
Kiln-HA4
|
159 mg/g for Fe3+
175 mg/g for Fe3+
147 mg/g for Fe3+
172 mg/g for Fe3+
|
204 mg/g for Mn2+
344 mg/g for Mn2+
243 mg/g for Mn2+
232 mg/g for Mn2+
|
99% Fe
96% Mn
|
This study
|
Cost assessment of synthesized Nano- hydroxyapatite
If the source of Ca ions was affordable and free, the cost of manufacturing hydroxyapatite would be reduced. Waste cement bypass dust is a rich source of both CaCO3 and CaO which can be utilized in the hydroxyapatite production process. As a result, the initial components are cheap, enabling the implementation of a low-cost method. Costs connected with the processing of Kiln-HA samples were largely driven by energy consumed. Making 100 g of hydroxyapatite from recycled cement bypass dust cost were around 184 EGP/100g (9.32 € /100g). Table 16 displayed the price for manufacturing100 g of hydroxyapatite from different calcium sources. The hydroxyapatite powders from cement bypass were lower than those published by Sigma Company. The manufacturing of powder nano- HA price (surface area > 80 m2/g) published by Sigma-Aldrich company were 462 € which equal 9162 (EGP) (Aldrich, 2022).
Table 16
The total cost of the materials that were tested
hydroxyapatite Samples
|
Cost/100g by (EGP)
chemicals - electricity
|
Price of HA
(€/100 g)
|
References
|
HA-Cl
|
258 £
|
13.02 €
|
(Safaa El‑Nahas, 2022)
|
HA-NO3
|
386 £
|
19.47 €
|
HA-OH
|
263 £
|
13.27 €
|
HA-AC
|
278 £
|
14.02 €
|
Kiln-HA
|
184 £
|
9.32 €
|
This study
|
HA® Sigma-Aldrich
|
9162 £
|
462 €
|
(Aldrich, 2022)
|
Field Study in Real Water Samples
Two groundwater well samples were taken that had already been polluted with iron and manganese ions at quantities of 2.46 and 0.11 mg/L, respectively. For polluted water with Fe and Mn ions, all of the examined hydroxyapatite samples had a clearance efficiency of 99.9%.The results reveal that hydroxyapatite materials may remove Fe and Mn ions from groundwater wells even when other cations (such as Ca, Mg, and Al) or anions (e.g.: NO3, NO2, SO4, Cl, SiO4) are present. A variety of goals, such as the accessibility of clean drinking water via cheap adsorbent materials as well as the sustainable management of our waste resources, were achieved in this study.