Green Fabrication of SnO2 Nanoparticles and Uses as a Powerful Sorbent for Malachite Green Detoxification

Introduction

Organic dyes, utilized in various fields, are both toxicants and carcinogens. The toxic effects of organic dyes on humans and the environment exist even at low concentrations. The persistence of organic dyes in the aquatic environment fosters severe public health and environmental problems¹. Dyes exhibit chemical stability and structural resistance to biodegradation in both solid and aqueous environments, resulting in a prolonged life cycle upon their discharge into the environment². Accumulation of dyes in aquatic environments can result in bioamplification, leading to a harmful impact on the food network³. Depending on the rearrangement of dyes in the environment and the formation of other dye complexes, certain initial dyes may undergo a conversion into more harmful compounds instead of retaining their original form⁴. Furthermore, it should be noted that the mere presence of fluorescent hues can have an impact on the behavioral and reproductive characteristics of fish and certain other aquatic species⁵. Malachite green is a bright green artificial compound belonging to the triphenylmethane dye family. It finds widespread application in the textile, paper, and aquaculture sectors. Malachite Green, when introduced into the aquatic environment, exhibits high toxicity and poses substantial threats to public health. It has been determined that this substance has the potential to cause cancer in people, can cause genetic mutations in certain creatures, and can damage DNA, which may lead to the development of cancer. Even exposures that are not lethal can potentially result in the development of liver tumors, lung tumors, and cancers in the thyroid gland⁶. Therefore, it is crucial to assess the efficacy of malachite green removal in water to prevent potential environmental catastrophes and safeguard human well-being. Numerous methods have been developed for the removal of malachite green from effluents; these include adsorption, advanced oxidation processes, and membrane separation^7,8. However, conventional methods possess some limitations, notably low efficiencies, high operational costs, or production of secondary pollutants⁹. Nanotechnology is an alternative and promising approach to treating water pollution due to its properties and higher efficiencies^10,11,12,13. Nanomaterials, such MgO , ZnO , Al2O3.g-C3N4 ,carbon nanomaterials, and magnetic nanoparticles, exhibit high surface area, enhanced reaction properties, and tunable surface chemistry that can improve the ability to absorb and degrade malachite green^14,15. They exhibit greater removal efficiencies with lower doses of material, may be applied and reused multiple times, and may be able to concentrate the dye molecules more easily than traditional materials¹⁶. Furthermore, nanotechnology could lead to developing novel and sustainable water treatment techniques to more effectively address dye pollution¹⁷.

The use of plant extract methods for synthesis can be more easily scaled up when large-scale applications are required and does not pose problems associated with the need for specialized/cultivation equipment or with special cultivation conditions that are often required when working with microbial systems^18,19. Synthesis is generally simpler and less complicated than the microbial process and does not require the complex cultivation conditions of bacteria and fungi²⁰. Plant extracts are therefore more accessible to the user and less labor intensive. Some bacteria and fungi used in the synthesis of nanoparticles are pathogenic or have toxic metabolites that can cause health problems²¹. The pathogenic factors associated with handling and disposal are becoming so significant that safe disposal methods are required²². In addition to the pathogenic factors, the biology and by-products in the reaction mixture could also limit the purification options, resulting in a low level of purity and additional steps to purify the particles²³.Therefore, in this work, we present a simplistic, eco-friendly, green, and cost-effective scheme for the synthesis of SnO2 nanoparticles using plant extract of Juniperus Procera and assessed their applicability as sorbent materials for the removal of malachite green (MG) dye.

Methodology

Preparation of Juniperus Procera Extract

Fresh and green Juniperus Procera leaves were harvested from trees of a plantation field located in the peri-urban zone in Al-Rass city of Saudi Arabia. The collected leaves were repeatedly rinsed with distilled water to remove impurities, dirt and substances adhering to the leaves and dried at room temperature for 7 days. The dried leaves (0.375 kg) were crushed in a mechanical blender and the resulting powder was used for the extraction process. Then the powder was mixed with 2 liters of deionized water with constant stirring (200 rpm) for one day at   ambient temperature and humidity. Filtration was performed afterward via Whatman No. 1 filter paper and the extract was saved in a reagent bottle at room temperature to for the purpose of serving as a reducing factor in the production of SnO2 nanoscale.

Preparing samples for time and pH dependent Studies

For the time-dependent adsorption experiments, small portions of the SnO2 nanoscale solution were collected at various intervals, ranging from (0 to 360 minutes), to observe how the adsorption efficiency changes over time. After each time point, the samples were filtered using Whatman No. 1 filter paper to separate the adsorbent from the liquid. The concentration of the target analyte in the clear filtrate was then measured. For the pH-dependent studies, the pH of the SnO2 nanoscale solution was carefully adjusted to several values—specifically 3, 5, 7, 9, and 11using either   HCl or NaOH. Once the pH was set, the solution was stirred continuously, typically for about 60 minutes, to ensure ample interaction between the adsorbent and the analyte. Just like in the time-dependent studies, the samples were filtered with Whatman No. 1 filter paper, and the filtrates were analyzed to assess how the adsorption efficiency varied across different pH levels.

Fabrication and of Green SnO2 Nanoparticles

To fabricate green SnO2 nanoparticles, 90ml of SnCl2 (0.2M) was stirred slowly with 30 mL of the extract of Juniperus Procera and ultra-sonication for 15min. Afterward, the mixture was agitated for an extra 24h. Ultimately, 25ml of KOH (3M) was added dropwise to the mixture until the precipitate formed and the obtained precipitate was isolated from the mixture using vacuum filtration. The yellowish powders were dried at 100^oC for 3h and calcined in a muffle furnace at 400^oC for 1h.

Characterization of Green SnO2 Nanoparticles

A XRD pattern was acquired to determine the crystalline nature of the manufactured SnO2 nanopowder. A diffractometer (D8 -Advance -Bruker) was operated to examine the samples with (Cu-Kα) radiation at a wavelength of 0.154 nm. The morphology of the gotten nanopowder was observed under SEM. The specimens were earlier oven-dried at (105)^oC and covered with (gold) to generate electrical passage on SnO2 nanopowder. The optical properties and diffuse reflectance spectra (DRS) of the SnO2 were determined by spectrophotometer in range 0f (300-800nm). The mode of stretching and vibration in the prepared SnO2 was studied by FTIR (Nicolet).

Adsorption testing

To assess the adsorption situations for MG dye onto SnO2 nanoparticles, the vestige of sorption period, sorbent mass, pH7, and initial dye quantity were examined. For the sorption investigation, 150 ml of MG dye (10 mg/L) was mixed with 60 mg of SnO2 in a 250 ml beaker and stirred continuously at pH 9. Samples were taken at different time intervals from 1 to 480 min. Afterward, the samples were filtered, and the concentration was measured. Formula (1 and 2) were utilized for the computing elimination rate (R%) and adsorption capacity (qt) of MG dye²⁴.

Results and Discussion

Characterization of SnO2 Nanoparticles

The SEM image (figure1a) demonstrates that the nanoparticles display an irregular shape, in that they appear somewhat granular or agglomerated. From the image, it can be seen that the nanoparticles appear to be bunched together into an aggregate that does not possess a geometrically defined shape such as a sphere or a rod. Their surfaces appear rough, and some of the particles appear to have cracks or voids in between. This could be due to the sample preparation. To determine a representative particle size, several particles were manually measured from the SEM images using a ruler. The average particle size obtained from these measurements is 28.43 nm. Despite variations in particle shape and size, the measurements provide a reliable estimate of the average particle size²⁵. This approach ensures that the reported size reflects the overall particle distribution in the sample.

Analysis of the EDX spectrum (Figure 1b), indicates that 2 major elements: tin (Sn) and oxygen (O), which correspond to the two main elements of tin dioxide (SnO₂). The spectrum shows, that Sn(87%) has large intensity peaks at around 3-4 keV, the Kα and Lα lines of Sn. There is also a significant peak for O(33%), about 0.5 keV. The EDX characterization confirms the successful synthesis of SnO₂ nanoparticles, in which tin and oxygen are the two predominant contributors. The spectra efficiently confirm the presence of SnO₂, giving a nod to minor discrepancies from ideal stoichiometry²⁶. Figure (1c) displays the X-ray diffraction pattern of a SnO2 sample that was produced using Juniperus Procera extract and without it. The X-ray diffraction analysis of SnO2 reveals distinct peaks at (110), (101), (200), (211), (220), (002), and (301) at diffraction angles of 2µ, specifically at angles of 26.5 o, 33.96 o, 38 o, 45.2 o, 54.8 o, 66.1 o, and 75.2 o, respectively. The peaks suggest the possibility of cassiterite crystals with a tetragonal rutile structure, as documented in JCPDS No. 411445²⁷. The (110) plane displays a prominent peak of great intensity. FTIR   of the SnO2 samples, as produced, show peaks at 3409 cm1 and 1630 cm-1. These peaks caused from the stretching of the water surface hydroxyl group absorbed (Fig. 1d). A high spectral intensity was observed for SnO2(extract), while SnO2(P) showed a diminishing intensity with increasing concentration. Reduced surface area and increased crystallinity of SnO2 contributed to the decline in spectral band intensities. In the symmetric O-Sn-O stretching mode, the oxide-bridge functional group was determined as the source of the vibration absorption band observed at around 590 cm²⁸. X-ray diffraction (XRD) analysis determined an average crystallite size of 32.05 nm, calculated using the Scherrer equation., which is greater than the scanning electron microscopy (SEM) particle size of 28.42. This indicates that the crystallite size determined by XRD is measuring coherent diffraction domains while the SEM size measure is for the complete particle size. The difference is reasonable because XRD typically measures strictly crystallites within the samples particles, whereas SEM captures all dimensions of the particle shape itself, which is aligned and attests to the consistency of the structural and morphology measurements²⁹.

Figure 1: (a) surface morphology of SnO2;(b) EDX analysis of SnO2;(c) XRD pattern of SnO2:(d) FT-IR spectrum of SnO2 Click here to View Figure

Brunauer-Emmett-Teller (BET) analysis

The BET method was employed to estimate the specific surface area of SnO₂ by analyzing the gas adsorption and desorption isotherms. The porosity of the SnO₂ nanoparticles was also characterized through nitrogen gas adsorption and desorption processes. Figures 2a and 2b display the adsorption and desorption isotherms, which resemble a Type IV isotherm. This isotherm is typical of mesoporous materials, known for undergoing monolayer and multilayer adsorption, leading to hysteresis and capillary condensation. Consequently, the isotherm analysis confirmed the mesoporous nature of the synthesized SnO₂ nanomaterials³⁰. Further evidence of this mesoporosity was provided by the pore size distribution, also shown in Figure 2b. A significant proportion of the pores had a diameter of approximately 1.84 nm. Although the material exhibited only a limited presence of macroscale pores, the pore distribution extended up to 250 nm. The specific surface area of the material was measured to be 5.59 m²/g. All in all, the analysis clearly points to a mesoporous structure with a diverse pore size distribution, which could have significant implications for the material’s potential applications.

Figure 2: Nitrogen adsorption-desorption isotherm of SnO₂ nanoparticles (a) BET surface area (b) Pore size distribution. Click here to View Figure

Contact Time Analysis

The test was done to find out the optimal contact duration required for eliminating MG dye. Adsorption efficiency experiments were undertaken by utilizing various contact durations from( 0 to 360 minutes), as given in( Fig 3). The result demonstrates that the capacity of adsorption rose up from 117.4 to 468.9 mg/g as the contact duration increased from 3 minutes to 120 minutes. The quick rise in the absorption of pigments could be caused by surface active sites on SnO2 nanoparticles to absorb MG³¹. Over time, the operation of adsorbing dyes slowed down and eventually reached equilibrium. This phenomenon can be explained by the saturation occurring on the surface of the SnO2 nanosorbent and the repulsive force between the MG dye on the SnO2 nanosorbent surface¹⁵. The highest removal percentage seen was 483.1 % after 120 minutes.

Figure 3: Relationship between the adsorption capacity over time for various initial concentrations. Click here to View Figure

pH analysis

The pH of the media strongly influences the sorption operation, as it affects both the active site and the metal’s chemical state²¹. The pH_pzc, or point of zero charge, is the pH value at which the surface of the adsorbent attains electrical neutrality²². The adsorbent surface exhibits a positive charge, as evidenced by its pHpzc value. This positive charge facilitates the adsorption of anions in solutions with pH levels below the pHpzc. Conversely, in solutions with pH values above the pHpzc, the surface acquires a negative charge, which improves cation adsorption. To determine the optimal pH for (MG) removal, pH values were systematically varied from 3 to 11. To acidify the solution, we added diluted HCl, and for basification, NaOH was used. This was done to check the concentration of each component using a pH meter. The MG removal effectiveness was assessed at each pH level under identical conditions to determine the pH at which malachite green removal considerably deviated from the surrounding levels.

From figure 4(a), a strong variability in the percentage removal of the contaminant with pH. A removal efficiency of about 15% is observed at pH 2, demonstrating that the adsorbent has a weak efficacy under acidic conditions. However, with increasing pH of 5, the removal percentages rise markedly to about 85%, which improves the adsorption efficacy. At a neutral pH of 7, the removal efficiency reaches a maximum close to 95%, while it peaks at about 98% at about pH 9, indicating that this is the optimum pH for adsorption. At a maximum pH of 11, the removal percentage is slightly less than before at about 90% to 95%. This could be due to changes in the surface charge of the adsorbent or possible ionization of the contaminant. This trend certainly highlights the effect of pH on the adsorption process, with maximum adsorption occurring at neutral to slightly basic pH conditions. At elevated pH levels, the adsorption of malachite green dye by SnO2 decreases due to the formation of hydroxyl ions and enhances the negative charge on the SnO2 surface. This reduces the electrostatic attraction between the positively charged dye molecules and the negatively charged surface. Additionally, high pH may lead to the formation of hydroxylated or polymeric species of the dye, which are less readily adsorbed onto the SnO2 surface. As a result, the overall adsorption capacity is diminished³². At low pH levels, strong interactions can occur between hydroxonium ions (H3O+) and the adsorbent surface. These interactions have the potential to decrease the efficiency of removing MG.

Figure 4: Effect of pH on removal efficiency and final pH change (a) Removal percentage of MG as a function of pH Click here to View Figure

Kinetics modeling of MG dye elimination

To assess the MG dye elimination process via SnO2 nanoparticle, the data obtained from the adsorption experiment is examined using multiple kinetics models including  the pseudo-first-order (PFO) and pseudo-second-order (PSO) models, intraparticle diffusion, and Elovich models were employed. The models are presented in the linear equation format as follows^33,34.

PFO kinetics suggests that adsorption occurs by physical adsorption, whereas PSO assumes chemical adsorption (chemisorption). Chemisorption involves creating a chemical bond between an adsorbent surface and an adsorbate. Alternatively, physical adsorption is characterized by relatively gentle interactions such as hydrogen bonding, van der Waals forces, and electrostatic interactions between adsorbant and adsorbate³⁵. The intraparticle diffusion model Infers that the adsorbate’s internal diffusion is the slowest step, resulting in the rate controlling step during the sorption operation, and that adsorption is instantaneous. Elovich model is applied in adsorption kinetics to explains the chemical reaction process in nature²⁹. Table (1) summarizes the parameters of utilized kinetic models. From the obtained results the PSO R² (0.999)   (figure 7a) fitting is greater than PFO fitting (figure 5b) and   Elovich model fitting (figure 5c)   and Intra-particle Diffusion model fitting (figure 5d). Therefore, the process of adsorption follows pseudo-second-order kinetics mainly because it is limited by chemisorption that occurs because stronger chemical bonds form between the adsorbate and the adsorbent. The model relies on the premise that the reaction rate is proportional to the square of the unoccupied sites, which often yields better fits for experimental data in complex surface interaction systems, or at higher concentrations of adsorbate. Furthermore, the PSO model’s computed qe is consistent with the measured qe than the PFO model. Hence, PSO is more suitable for explaining the adsorption kinetics of MG dye than PFO . This indicates that the dye sorption on SnO2 nanosorbent is mainly mediated by chemisorption.

Figure 5: Kinetics models of adsorption of MG via SnO2 nanoparticle;(a) PFO; (b)PSO; (c) Elovich ;(d) Intra-particle Diffusion Click here to View Figure

Table 1: Kinetic parameters and correlation coefficients (R²) for different adsorption models at various initial concentrations

Kinetics Model	Parameter	25 ppm	75 ppm	200 ppm
Pseudo-first-order	qe	14.53	91.51	226.59
	K1	8.3×10-3	12.9×10-3	10.3×10-3
	R2	0.947	0.952	0.925
Pseudo-second-order	qe K2 R2	66.48	205.30	500.01
		12.05×10-3	0.57×10-3	0.07×10-3
		0.999	0.999	0.999
Elovich	β	0.256	0.034	0.014
	α	1304.70	334.04	320.42
	R2	0.929	0.966	0.969
Intra-particle Diffusion	Kdif1 C1 R2 Kdif2 C2 R2	115.22	33.79	8.08
		40.94	7.61	32.54
		0.985	0.956	0.998
		23.18	8.37	0.88
		213.81	102.40	53.01
		0.998	0.993	0.880
	Kdif2	2.79	1.07	0.39
	C2	435.25	181.09	59.018
	R2	0.999	0.981	0.984

 

Isotherm Data Analysis

Four models including{ Langmuir, Freundlich, Tempkin, and Dubinin–Radushkevich (D–R)} were used to describe the experimental adsorption data^36,37.

In Figure 6, the Langmuir, Freundlich, Tempkin, and Dubinin–Radushkevich (D–R) isotherms are used to determine the relationship between adsorption and liquid-phase concentration. To determine the most suitable isotherm equation, linear regression is commonly used, and correlation coefficients are used to evaluate the suitability of the equations. The examination of SnO2 nanoparticle adsorption behaviors on MG removal revealed that the Freundlich isothermal model, with an R2 value of 0.987, adequately explained the data. The equilibrium models parameters   values of the Langmuir, Freundlich, Tempkin, and Dubinin–Radushkevich (D–R) isotherm are given in table (2).

The value obtained for qm was compared with other examined sorbent materials, as shown in Table (3). The results of the comparison shows that among the sorbents listed the SnO₂ nanoparticles are the most effective, making it a promising adrsorbent material for adsorption applications. The remarkable difference SnO₂ has in comparison to sorbents listed also highlights an advantage for utilizing advanced nanomaterials to improve adsorption efficiency. Advances like SnO₂ nanoparticles will lend themselves to a wide range of future environmental remediation applications. The comparison shows that continued research and development of nanotechnology is needed to improve adsorbents adsorption performance. The results reported here also demonstrate that nanomaterials outperform traditional adsorbents with great removal efficiencies. 

Figure 6: Linear  isotherm plots; (a) Langmuir;(b) Langmuir;(c) Temkin;(d) Dubinin- Radushkevich Click here to View Figure

Table 2: Equilibrium isotherm parameters and correlation coefficients (R²) for various adsorption models

Equilibrium Model	Parameter	Value	R2
Langmuir	qm	724.64	0.738
	KL	0.35
	RL	0.014
Freundlich	KF	168.76	0.987
	n	1.54
Temkin	B	25.48	0.779
	KT	4.68X10-4
Dubinin- Radushkevich	qm	14.90	0.757
	b	4.54×10-8
	E	9.06×10–4

 

Table 3: Comparison of adsorption capacities of various sorbents for malachite green dye removal.

Sorbent	Adsorption capacity (mg/g)	Reference
Zeolitic imidazole frameworks	164.9	38
Carbon (nanotube )	80.60	39
Halloysite (nanotubes)	99.60	40
Activated carbon	27.80	40
Palm  (fibre)	149.40	41
Rubber -wood	36.50	42
Jute fiber(carbon)	136.60	43
fly ash	170.00	44
NiO / nanoflakes MgO SnO2 nanoparticles	142.10 232.30 724.00	45 46 Current study

Conclusion

A sustainable and cost-effective method was developed to synthesize nanoscale SnO₂. A series of batch adsorption tests of SnO₂ nanoparticles were conducted at different contact times, pH, and initial dye concentrations to assess the effectiveness of SnO₂ nanoparticles at the removal of malachite green (MG) dye. The results show that the SnO₂ nanoparticles are fast absorbers and have a high removal capacity, with a maximum experimental adsorption capacity of 724 mg/g. The kinetics of adsorption were well fitted to the pseudo-second order model (R² = 0.999), indicating that chemisorption was the rate-controlling phenomenon. The adsorption isotherm of MG was well described using the Freundlich model (R² = 0.987), suggesting that the surface sites available for adsorption on the nanoparticles are covered to some extent, thus indicating adsorption activity on a heterogeneous surface. This study confirms that the adsorption of MG on SnO₂ nanoparticles occurs through electron transfer/sharing between a surface nanomaterial and the dye – thus further confirming the reasonableness of the adsorption capacity and mechanism.

Acknowledgement

The authors extend their appreciation to the Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia, for funding this research work through Grant No. (221412017).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding Sources

There are no funding Sources

References

1. Ikram, Z., et al. ACS Omega, 2024, 9(9), 10017-10029.
   CrossRef
2. Ahmadi-Leilakouhi, B., et al. Chem. Pap, 2023, 77(2), 1033-1046.
   CrossRef
3. Gharavi-Nakhjavani, M. S., et al. Environ. Sci. Pollut. Res, 2023, 30(17), 48911-48927.
   CrossRef
4. Yan, Z., et al. Environ. Pollut. 2018, 239, 223-232.‏
5. Solayman, H. M., et al. J. Environ. Chem. Eng, 2023, 11(3), 109610.
   CrossRef
6. Dowling, G., et al. Anal. Chim. Acta, 2007, 586(1-2), 411-419.
   CrossRef
7. Ali, I., et al. J. Environ. Manage, 2021, 298, 113413.
8. Kumar, A., et al. Sep. Purif. Technol, 2022, 287, 120529.
9. Sharma, P., et al. Chemosphere, 2022, 303, 135070.
   CrossRef
10. Chen, L., et al. Sep. Purif. Technol, 2022, 278, 119563.
11. Ahmed, M. J., et al. J. Mol. Liq, 2021, 328, 115447.
    CrossRef
12. Zhao, S., et al. J. Environ. Chem. Eng, 2022, 10(5), 107218.
13. Liu, X., et al. Chemosphere, 2021, 284, 131356.
    CrossRef
14. Dutta, L., et al. Korean J. Chem. Eng, 2024, 41(3), 589-607.
    CrossRef
15. Saud, A., et al. ACS Omega, 2024, 9(27), 29088-29113.
    CrossRef
16. Khalatbary, M., et al. Biomass Convers. Bioref, 2024, 14(2), 2495-2513
    CrossRef
17. Zhao, Y., et al. J. Clean. Prod, 2021, 292, 126032
    CrossRef
18. Alghamdi, M. D., et al. Inorg. Chem. Commun, 2024, 159, 111855.
    CrossRef
19. Akhtar, M. S., et al. ACS Sustain. Chem. Eng. 2013, 1(6), 591-602.
    CrossRef
20. Mittal, A. K., et al. Biotechnol. Adv. 2013, 31(2), 346-356.
    CrossRef
21. Ahmed, S., et al. J. Radiat. Res. Appl. Sci. 2016, 9(1), 1-7.
22. Joudeh, N., & Linke, D. Nanoscale Adv. 2020, 2(8), 3336-3347.
23. Zhang, X., et al. Environ. Sci. Nano 2016, 3(6), 1274-1282
24. Singh, P., et al. J. Nanopart. Res. 2016, 18(5), 166
25. Zhang, Z., et al. Appl. Surf. Sci, 2021, 541, 148425.
26. Kumar, S., et al. Ceram. Int, 2022, 48(8), 11415-11423
27. Rani, N., & Jaggi, N. Bull Mater Sci. 2020, 43(1), 146.
    CrossRef
28. Zhao, W., et al. Opt Mater.2022, 123, 111934
    CrossRef
29. Patel, H. A., et al. Ceram. Int, 2021, 47(16), 22922-22930
30. D’Arienzo, M., et al. Chem Mater.2013, 25(18), 3675-3686.
    CrossRef
31. Naggar, A.H., et al. Environ Sci Pollut Res. 2023, 30(49), 108247-108262.
    CrossRef
32. Sheerazi, Z., et al. Biomass Conv Bioref.2023, 1-20.
33. Al-Ghouti, M.A., et al. J Hazard Mater. 2010, 176(1-3), 510-520.
    CrossRef
34. Órfão, J.J.M., et al. J Colloid Interface Sci. 2006, 296(2), 480-489.
    CrossRef
35. Adeyemo, A.A., et al. Appl Water Sci.2017, 7, 543-568.
    CrossRef
36. Ismail, W.N.W, et al. Civil Eng J.2022, 8(9), 1787-1798.
    CrossRef
37. Heidarizad, M., & Şengör, S.S. J Mol Liq. 2016, 224, 607-617.
    CrossRef
38. Ezzati, R. Chem Eng J.2020, 392, 123705.
    CrossRef
39. Tseng, Ru-Ling, et al. Chem Eng J. 2014, (237), 153-161.
    CrossRef
40. Rodríguez-Narciso, S., et al.Adsorpt Sci Technol. 2021, 2021, 5522581.
41. Wang, J., & Guo, X. J Hazard Mater.2020, 390, 122156.
    CrossRef
42. Duran, C., et al. J Chem Eng Data.2011, 56(5), 2136-2147.
    CrossRef
43. Lin, K. et al. Chemosphere. 2015, 139, 624-631.
    CrossRef ‏
44. Ghaedi, M., et al. Phys. Chem. Chem. Phys. 2016, 18(19), 13310-13321.
    CrossRef ‏
45. Kiani, G., et al. Appl. Clay Sci. 2011, 54(1), 34-39.‏
46. Santhi, T., et al. Hazard. Mater. 2010, 179(1-3), 178-186.
    CrossRef ‏
47. Hameed, B. et al. J. Hazard. Mater. 2008, 154(1-3), 237-244.
    CrossRef ‏
48. Kumar, K. et al. Dyes Pigm. 2007, 72(1), 124-129.
    CrossRef ‏
49. Porkodi, K., Kumar, K. V. J. Hazard. Mater. 2007, 143(1-2), 311-327.
    CrossRef ‏
50. Mall, I. et al. Colloids Surf. A: Physicochem. Eng. Asp. 2005, 264(1-3), 17-
    28.‏
51. Wei, A., et al. Chem. Eng. J. 2014, 239, 141-148.
    CrossRef ‏
52. Idriss, H. J. Optoelectron. Biomed. Mater. 2021, 13(4), 183-192
    CrossRef

---

This work is licensed under a Creative Commons Attribution 4.0 International License.