3.1. Characterization of adsorbents
Figure 2 depicted the material's surface as coarse, porous, and riddled with holes. The sizes of the holes were quite uniform that facilitated the adsorption process. This material had many holes so the adsorption process took place and achieved good adsorption efficiency in a short time. The S4800 Field Emission Scanning Electron Microscope was applied for visualize morphological features on the surface of materials (Hitachi, Japan).
The EDX spectrossic examination revealed that the primary components of the substance were carbon and oxygen at a volume rate of 66.47% and 33.53%, respectively. The above ratio was due to the activation of the material at 3000C – this process helped to convert organic compounds found in the leaves such as Lignin, Holocellulose, etc. into gases. These gases flew away leaving blanks, which was the porosity mechanism that facilitated the adsorption process.
Pine leaves are mostly made of cellulose and lignin, whose components serve as active sites for dye adsorption. To determine the active functional group on the surface of materials, FT-IR spectra of pine leaf biomass before and after dye adsorption were determined by Nicolet iS5 FT-IR spectrometer (Thermo Fisher, USA) and shown in Fig. 3. The spectra presented many peaks, indicating that pine leaves contain a variety of functional groups that may aid in the binding of dye molecules.
The absorbance bands of pine leaves biomass were discovered to comprise a large overlapping band at 3200–3500 cm− 1, with a peak at 3412 cm− 1, which can be attributed to hydroxyl group O-H stretching and intermolecular hydrogen bonding. This band's location and asymmetry suggested the presence of strong hydrogen bonds. This peak diminished following dye adsorption, showing that the O-H and hydrogen bonds in pine leaves biomass were disrupted in order to react with dye.
The signal at 2955 cm− 1 indicates vibration of CHn, mainly owing to C-CH2 and C-CH bonds, whereas a peak of 1735 cm− 1 presents the C = O group of the carbonyl in the hemicellulose (Zhu et al. 2016). The existence of C = C stretching of the phenol group is shown by the peaks at 1600 cm− 1. Whereas peaks at 1033 cm− 1 in the spectrum lead to a strong C-O bond due to the cellulose ether group (Silva et al. 2019).
Except for minor differences, the spectra of the dye-loaded pine leaves biomass exhibited comparable properties to the adsorbent in natural form. After the adsorption, the specific peaks are somewhat moved from their original positions, and the intensity changes. These findings indicated that functional groups contribute the adsorption of dye ions of the pine leaves biomass via weak electrostatic contact or Van der Waals interactions.
3.1. Survey results of zero charge point (pHpzc) and effect of pH on Dyes Adsorption
The pHpzc value was about 7.5 based on the material's zero-charge graph. This finding allowed us to anticipate the material's adsorption capability for MB and MO at any pH value. When the pH of the solution was less than 7.5, the material's surface was positively charged, and excellent anion adsorption was favored. If the solution pH was more than 7.5, the material's surface would have a negative charge and improved cation adsorption (Banerjee &Chattopadhyay 2017). This was the basis of investigating the factors influencing pH on adsorption capacity to determine the optimal pH value of MB and MO.
Figure 5a depicts the fluctuation of MB and MO adsorption capabilities on biomass at various pH levels. From this figure, the adsorption capacity of MO rose as the pH value decreased, but the uptake of MB showed to the contrary. Clearly, the adsorption capacity of biomass towards MB and MO is significantly reliant on solution pH because it impacts the speciation of the dye as well as the adsorbent's surface charge distribution in the solution. In other words, it might affect the electrostatic interactions of an attracting or repulsive nature between the dye species in the solution and the surface of the biomass of pine leaves.
Noticeably, there was a tendency to increase the adsorption capacity of MB from pH = 2 to 8, prior to equilibrium at pH > 8. When dissolved in water, MB, a base-cationic dye, decomposed into MB+ ions, and the lower the pH value (greater concentration of H+ in the solution), the adsorption competition between MB+ and H+ lowered the material's MB adsorption capacity (Khnifira et al. 2022). Furthermore, carbonyl groups (C = O) and hydroxyl groups (O-H) on the adsorbent's surface can bind cationic dye molecules at the high pH. As a result, the biomass of pine leaves has a high MB adsorption capability in an alkaline environment (Yagub et al. 2012).
The adsorption of MO onto biomass reduced from 40.28 to 35.50 mg.g− 1 when the pH climbed from 2 to 10, indicating that anionic dye adsorption onto pine leaves is pH-dependent and preferential under acidic conditions. The increased anionic dye adsorption at lower pH levels has been linked to the full protonation of the biomass surface functions, which provides strong electrostatic attraction to the MO dye molecules, allowing the dye to be quickly absorbed (Zubair et al. 2017). In contrast, increasing the pH causes an increase in anion OH− in the solution, which competes with anionic dye molecules, resulting in a drop in the qe of MO on biomass. This shows that electrostatic and chemical interactions were predominantly responsible for the uptake of anionic dyes onto pine leaf biomass (Zubair et al. 2020).
In order to understand how pH affects adsorption behavior, the zeta potential of the adsorbent was measured at various pH values. As demonstrated in Fig. 4, when the pH was less than 7.5, the biomass’s surface potential was positive, and the uptake of anion MO was improved due to electrostatic attraction. In contrast, the surface potential of biomass was negative, which was suitable for the uptake of cation MB. However, the adsorption capacity of MB was not diminished when the pH value is higher than 9. It indicates that the adsorption mechanism of MB follows not only electrostatic interaction but also hydrogen bonding between adsorbent and adsorbate. The biomass may be considered an H-donor, whereas MB was an H-acceptor. This mechanism can also occur during MO adsorption.
According to the foregoing explanations, the optimal pH value of the material's MB and MO adsorption processes was 8 and 6. The study's findings were aligned with the findings of establishing the material's point of zero charges. Previous research has also observed similar behavior (Alseddig et al. 2017, Yang et al. 2018). Furthermore, based on Fig. 5a, it was inferred that the adsorption capacity of MB was larger than the adsorption capacity of MO at the ideal pH value. The degree of diffusion was governed by the size or shape of the molecule in organic dyes with complicated and bulky structures, such as MB and MO. To describe dye adsorption capability, use the structure and molecular weights of dyes (Sun et al. 2022).
3.3. Effect of contact time and the initial dyes concentration
Because the contact duration of the dyes biomass adsorption process was a significant factor in the adsorption system design, Fig. 5b depicts the effects of adsorption time on the adsorption capacity of anionic and cationic dyes.
According to these curves, the adsorption of the molecules of MB and MO on the pine leaves is quick during the first 60 min to reach the equilibrium, which is 150 min for the MB and MO with maximum adsorbed amounts of 44.27 mg.g− 1 and 40.97 mg.g− 1, respectively. The quick adsorption rate of the initial phase (fast phase) may be explained by the reality that he adsorption sites on the surface of our adsorbent are free at the start of the phenomenon. Once the dye molecules are immobilized, they block the pores, decreasing the rate of adsorption and resulting in a saturated array, which is why the second phase exists (slow phase). This trend is consistent with other research' conclusions (Alseddig et al. 2017, Ma et al. 2012).
At room temperature, Fig. 6a depicts the adsorption capabilities versus various starting dye concentrations. As the dye concentration rises from 100 to 500 mg.L− 1, the quantity of dye adsorbed (mg.g− 1) of both MB and MO rose from 44.28 mg.g− 1 to 127.23 mg.g− 1 (MB) and from 40.98 mg.g− 1 to 118.62 mg.g− 1 (MO). The concentration of the dye influences dye removal. The mass transfer driving force rose as the starting concentrations increased, as did the interaction between dyes and adsorbent, resulting in increasing adsorption capacity.
As shown in Fig. 6b, raising the initial dye concentration from 100 to 500 mg.L− 1 reduced removal from 88.55–50.89% (MB) and 81.95–47.45%. (MO). This was discussed in detail that the adsorption capacity was complete and the adsorption efficiency was high when the concentration of MB and MO was low due to the low and constant weight of the adsorbent. Notwithstanding, the amount of MB and MO was very large at the high concentration, exceeding the number of fixed adsorption centers in the material, thus these uptake were incomplete, resulting in a decline in adsorption efficiency (Zhu et al. 2016, Zubair et al. 2020).
3.4. Adsorption mechanism of MB and MO onto biomass of pine leaves
In addition to taking into account kinetics and isotherms models, as well as the characterisation of wasted biomass following the uptake of the dye, the mechanism of interaction of anionic and cationic dye molecules with biochar surface was further studied. The discussion of the findings is in the section below.
3.4.1. Adsorption equilibrium
Adsorption isotherms display the distribution of adsorbent molecules between the liquid and solid phases when the uptake obtains equilibrium. Normally, an appropriate model can be determined by fitting experimental data with several isotherm models, such as Langmuir, Freundlich, Sips, and Temkin (Ertugay &Malkoc 2014). The fitted plots from each isotherm for the adsorption of MB and MO onto biomass of Pinus kesiya were displayed in Fig. 7. Table 1 reports the isotherm parameters discovered via non-linear regression analysis.
Table 1
Calculated parameters (± SD) for MB and MO biosorption by Pinus kesiya biomass in single systems, acquired by non-linear regression analysis
Models
|
Parameters
|
MB
|
MO
|
Langmuir
|
KL (L.mg-1)
|
0.0268 ± 0.0061
|
0.0228 ± 0.0031
|
qmax (mg.g-1)
|
150.67 ± 8.73
|
142.72 ± 5.13
|
R2
|
0.9937
|
0.9981
|
Freundlich
|
n
|
3.35 ± 0.60
|
3.10 ± 0.40
|
KF (Ln.g)
|
26.41 ± 6.95
|
21.27 ± 4.44
|
R2
|
0.9872
|
0.9939
|
Sips
|
qs (L.g-1)
|
1.88 ± 2.90
|
2.31 ± 2.05
|
αs (L.mg-1)
|
0.0133 ± 0.0193
|
0.0168 ± 0.0136
|
βs
|
1.22 ± 0.44
|
1.10 ± 0.25
|
R2
|
0.9930
|
0.9979
|
Temkin
|
KT (L.mg-1)
|
0.3298 ± 0.1675
|
0.2508 ± 0.0724
|
bT (J.mol-1)
|
81.07 ± 11.45
|
82.49 ± 7.02
|
R2
|
0.9913
|
0.9971
|
The Langmuir adsorption model explains the MB and MO dye adsorption on the biomass of pine leaves reasonably well. The Langmuir model's coefficients of determination (R2) for the MB and MO biosorption data (R2 = 0.9937 for MB and R2 = 0.9981 for MO) were greater than those for the other models. It was discovered that biomass has a maximum adsorption capacity of 150.67 mg.g− 1 (MB) and 142.72 mg.g− 1 (MO), confirming physical monolayer adsorption. The homogeneous distribution of active sites on the adsorbent surface, which is implied by the Langmuir equation, maybe the reason why the Langmuir isotherm closely matches the experimental results (Sen et al. 2011).
According to the nonlinear Freundlich isotherm, determining the constant KF (Ln.g) and n coefficient (Table 1). This coefficient between 1 and 10 was a favorable range for the biosorption process. This proved that pine leaves (Pinus kesiya) were good biosorbent materials for MB and MO in an aqueous solution.
Additionally, the Temkin model's energy values bT calculations were less than 8 kJ/mol, which supported the idea that these adsorbents underwent physical adsorption at the measured temperatures.
Table 2
The maximum adsorption capacity of MB and MO calculated from the Langmuir model using various biosorbents.
Source of adsorbents
|
Adsorbent
|
qmax
(mg.g− 1)
|
Reference
|
Millettia thonningii seed pods
|
MB
|
14.09
|
(Jasper et al. 2020)
|
Casuarina equisetifolia pines
|
MB
|
41.35
|
(Chandarana et al. 2021)
|
Peanut shells
|
MB
|
67.42
|
(Benjelloun et al. 2022)
|
Walnut shells
|
MB
|
101.43
|
(Benjelloun et al. 2022)
|
Potato (Solanum tuberosum) peel
|
MB
|
105.26
|
(Guechi &Hamdaoui 2015)
|
Pine leaves
|
MB
|
150.67
|
This study
|
Corn leaves
|
MO
|
13.85
|
(Fadhil &Eisa 2019)
|
Dragon fruit foliage
|
MO
|
17.67
|
(Haddadian et al. 2013)
|
Pineapple leaf
|
MO
|
47.62
|
(Kamaru et al. 2015)
|
Populous leaves
|
MO
|
90.44
|
(Shah et al. 2021)
|
Shaddock peels-
|
MO
|
94.59
|
(Tao et al. 2019)
|
Pine leaves
|
MO
|
142.72
|
This study
|
The data in Table 2 compares the adsorption capabilities of several biosorbents that have been previously reported in the literature to those of pine leaves. The Pinus kesiya biomass that was presented in this study functioned superbly, outperforming the majority of other comparable adsorbents that had previously been described. As a result, pine leaves provide MB and MO from an aqueous solution a potential, cost-effective biosorbent.
3.4.2. Adsorption kinetics
In order to evaluate the effects of adsorption time on the uptake as well as the potential processes involved in removal, kinetic models were investigated (Villabona-Ortíz et al. 2022). Figure 8 depicts the fitting of experimental data to the pseudo-first-order, pseudo-second-order, Elovich, and intra-particle diffusion models using non-linear regressions.
Both cationic and anionic dyes were shown to quickly bind to biomass in the first few minutes of exposure. There are various steps to the process. Within the first minute, the surface adsorption occurs and dye molecules were transferred from the bulk phase to the surface of biomass. After that, dyes are injected into the pores of the material. Lastly, the equilibrium stage occurs and leads to a layered process (Jiang &Hu 2019).
Therefore, interparticle diffusion takes part in the adsorption procedure but is unable to regulate the total adsorption of dye molecules. As a result, the adsorption mechanism is driven by many mechanisms (Villabona-Ortíz et al. 2022).
The characteristics in Table 2 show that all models successfully fit the kinetics of MB and MO on biomass, with an overall R2 > 0.99 (Villabona-Ortíz et al. 2022). As a result, it can be concluded that the process is regulated by chemisorption and diffusive mechanisms. Additionally, the pseudo-second-order model provided an outstanding way of determining the experimental value of qe by that determined by the model, accurately describing the experimental data. Additionally, the fact that the intra-particle diffusion model's value of C is not zero, indicating that the sorption follows not only the intra-particle diffusion but also one or more additional diffusion processes in addition to the intra-particle diffusion (Dinh et al. 2018).
Table 2
Kinetics parameters on the uptake of MB and MO of biomass of pine leaves
Models
|
Parameters
|
MB
|
MO
|
Pseudo-first-order
|
k1 (min-1)
|
0.0594 ± 0.0102
|
0.0697 ± 0.0148
|
qe,cal (mg/g)
|
42.85 ± 1.1590
|
39.12 ± 1.22
|
R2
|
0.9991
|
0.9990
|
Pseudo-second-order
|
k2 (g.mg-1.min-1)
|
0.0021 ± 0.0004
|
0.0028 ± 0.0006
|
qe,cal (mg/g)
|
46.15 ± 0.98
|
41.92 ± 1.05
|
R2
|
0.9997
|
0.9996
|
Elovich
|
α (mg.g-1.min)
|
55.54 ± 23.82
|
91.23 ± 42.45
|
Β (mg.g-1)
|
0.1677 ± 0.0126
|
0.1991 ± 0.0145
|
R2
|
0.9998
|
0.9999
|
Intraparticle – diffusion
|
kd (mg.g-1.min)
|
1.3436 ± 0.1401
|
1.1347 ± 0.0471
|
C
|
26.01 ± 1.71
|
25.23 ± 0.57
|
R2
|
0.9991
|
0.9999
|
Thus, it implies that chemisorption occurring due to chemical reactions between biomass surface functions and dye molecules may govern the sorption process of both anionic and cationic dyes on biomass (Dinh et al. 2019, Shooto et al. 2020). There were three main processes in the adsorption of MO and MB, which are as follows: On the surface of pine leaves, there are abundant active binding sites (oxygen functionalities) for dye molecules to bind to, indicating a fast rate of dye adsorption. There are also slow rates of dye adsorption, showing diffusion of dye molecules into biomass pores, and equilibrium-stage saturation of active sites. Similar outcomes were shown (Omer et al. 2022, Zhu et al. 2018, Zubair et al. 2020).
3.4.3. Temperature Effect and Thermodynamic Parameters
The biosorption isotherm is affected by temperature because it shows the thermodynamic equilibrium between dyes in aqueous solution and dyes biosorbed on the surface of biomass. Table 3 displays the findings of a study on the impact of temperature at various temperature levels (30, 40, and 50oC). The capacity of MB and MO to adsorb to biomass increased with temperature, indicating that the adsorption process was an endothermic one that occurred spontaneously (Jiang &Hu 2019).
ΔGo has a negative value, and its upward trend is consistent with the temperature rise. The fact that ΔHo values are all positive and show that temperature has a direct impact on adsorption behavior shows that the uptakes of both dyes are spontaneous and that thermodynamic adsorption is beneficial. As the temperature rises, the adsorption capacity should grow as well. The ΔSo value was positive, indicating an increase in the degree of disorder at the interface between the dye solution and the biomass.
The ΔHo value can also be utilized to look into the adsorption's chemical or physical properties. The physical purification stage is crucial for van der Waals interaction when ΔHo = 20 kJ/mol. When 20 kJ/mol < ΔHo < 80 kJ/mol, the electrostatic contact plays a substantial role in the physical purification process. The adsorption mechanism is mostly chemical when ΔHo > 80 kJ/mol (Mosoarca et al. 2022). The biomass used in this experiment to purify MB and MO has a ΔHo value that is less than 80 kJ/mol, indicating that physisorption is at play, albeit a tiny chemical reaction may help the procedure.
Table 3
Thermodynamic parameters for adsorption of MB and MO onto biomass of pine leaves
|
T (K)
|
LnKC
|
∆G
(kJ/mol)
|
∆H (kJ/mol)
|
∆S (J/mol.K)
|
MB
|
303
|
1,44
|
-3,64
|
41,11
|
147,46
|
313
|
1,88
|
-4,90
|
323
|
2,45
|
-6,60
|
MO
|
303
|
0,85
|
-2,14
|
39,74
|
137,98
|
313
|
1,27
|
-3,30
|
323
|
1,83
|
-4,90
|