Kirti Srivastavaa*, Roli Vermaa, R.S.Jagadisha,
Yamini Pandeya, Abhishek Kumara
Abstract: Iron oxide nanoparticles (IONPs) are highly effective and environmentally sustainable adsorbents for the removal of heavy metals from wastewater. This study focuses on exploring a green route for synthesizing IONPs using Hibiscus rosa-sinensis petal extract, promoting an eco-friendly approach. The synthesized particles were evaluated analytically using XRD, confirming a crystallite size of 6.16 nm, and were assessed for adsorption of Pb(II) and Cu(II) ions. Optimized adsorption conditions, including pH 5 and an interaction period of 30 minutes, resulted in high removal efficiencies of 95.48% for Pb(II) and 84.9% for Cu(II). Adsorption behavior was best explained by the Langmuir isotherm, revealing maximum adsorption capacities of 30.77 mg/g and 28.74 mg/g for Pb(II) and Cu(II) respectively, while kinetic studies confirmed a pseudo-second-order model. These findings underscore the potency of green-synthesized IONPs as a promising and sustainable solution for wastewater treatment, paving the way for their potential real-world applications.
Keywords: Green iron oxide nanoparticles, Heavy metals, Adsorption, Chemical kinetics, Isotherm
Introduction
When discharged into natural water bodies, industrial effluent containing heavy metals poses serious risks to the health of people, animals, and plants. Heavy metals are persistent in soil and water and have been associated with severe health issues, including cancer. For example, lead exposure is linked to anemia and high blood pressure, while excessive copper can damage the liver, kidneys, heart, and brain. Recognizing these risks, the U.S. Environmental Protection Agency (EPA) has defined regulatory limits in drinking water for copper and lead at 1.3 mg/L and 0.015 mg/L, respectively. These concerns highlight the urgent need to remove Pb²+ and Cu²+ from aquatic environments (USEPA, 2007).
Conventional methods, such as evaporation, chemical precipitation, electroplating, ion exchange, and membrane processes, have been used to mitigate heavy metal contamination in wastewater (Fu, F. et al., 2011; BIS, 2012; Wang et al., 2009; Singh et al., 2019; Eyvaz et al., 2022). However, these techniques often fall short due to high costs and inefficiency at low metal concentrations. Adsorption has emerged as a cost-effective and selective alternative, utilizing materials like activated carbons (Deliyann et al., 2015), clay minerals (Gu, S. et al., 2019), chelating agents (Sabaté Reboll, J. et al., 2016), and chitosan/natural zeolites (Irannajad, M. et al., 2021), biological species (Srivastava, K. et al., 2022). Despite their promise, traditional adsorbents face limitations, necessitating the exploration of innovative and affordable alternatives with superior performance.
Low-cost adsorbents derived from industrial waste by-products, including fly ash (Al-Zboon, K. et al., 2011; Wang, S. et al., 2007), blast furnace sludge (Srivastava, S. K. et al., 1997), waste slurry (Namasivayam, C. et al., 1995), lignin (Carrott, P. J. M. et al., 2007), Fe(OH)3 (Namasivayam, C. et al., 1998) and red mud waste slurry (Gupta, V. K. et al., 2002; Almeida, A. C. M. et al., 2021) as well as agricultural residues like sawdust (Semerjian, L. (2018), areca waste (Subramani, B. S. et al., 2019), and tea factory waste (Nuhoğlu, Y. et al., 2021), and battery industry waste (Zhang, Y. et al., 2020) have been studied for heavy metal removal. While these materials are affordable and abundant, challenges such as scalability, activation wastewater management, and limited efficiency have hindered their widespread application (Irannajad et al., 2021).
Nanomaterials have demonstrated remarkable promise for the removal of heavy metals, especially nanoscale zero-valent iron (NZVI) and FeO4 (magnetite) nanoparticles. NZVI offers high surface area, reduced chemical requirements, and faster reaction rates, though it faces challenges like particle aggregation (Chen, S. Y. et al., 2008; Thirunavukkarasu, A. et al., 2020; Srivastava, K. et al., 2021). Magnetite nanoparticles, with their large surface area (Da Costa et al., 1994; Singh et al., 2017) and superparamagnetic properties (Sivashankar, R. et al., 2014), enable efficient heavy metal removal and easy separation using an external magnetic field. They are non-toxic, environmentally friendly, reusable, and maintain high efficacy after multiple regeneration cycles (Zargoosh, K. et al., 2013; Prasad, C. et al., 2017; Venkateswarlu, S. et al., 2014).
Green synthesis of iron nanoparticles, using biological systems such as plant extracts, inactivated plant tissue, exudates, and other components of live plants offers improved stability and environmental compatibility (Sugumaran, M. et al., 2012; Latha et al., 2014; Shojaee, S. et al., 2016; Gupta, K. et al., 2021; Nizamuddin, S. et al., 2019; Arora, R., 2019). Hibiscus rosa-sinensis, a widely known ornamental plant rich in bioactive compounds like flavonoids, tannins, and phenolics, has been identified as an excellent source for green synthesis. Its petal extracts provide an eco-friendly route to synthesize iron oxide nanoparticles with enhanced properties [42].
In this study, iron oxide nanoparticles have been synthesized using Hibiscus rosa-sinensis petal extracts through a green approach. These nanoparticles have been characterized using advanced analytical techniques, and their nanoscale structure has been confirmed. Their efficacy in removing Pb²+ and Cu²+ from wastewater has been evaluated under varying conditions of adsorbent dose, pH, and contact time. The adsorption behavior has been analyzed using Freundlich and Langmuir isotherm models, and additional kinetic studies have provided insights into the rate of adsorption. The potential of green-synthesized iron oxide nanoparticles as an effective, economical, and ecologically friendly method of eliminating heavy metals from aquatic ecosystems has been highlighted by this thorough study.
Materials and Methods
Preparation of Extract
Flowers from hibiscus plants were collected. Flowers were stripped of their stamens and calices, then their petals were dried in the shade and ground into a fine powder using a blender. The prepared powder was stored at 4 °C in an airtight container for future use. 5 g
of hibiscus petal powder was mixed with 100 mL of distilled water in an Erlenmeyer flask and the mixture was then heated at 60 °C for 1 hr. The mixture was then filtered using Whatman filter paper No. 1 and the filtrate was centrifuged for 15 minutes at 6000 rpm to take out tiny fragments. This extract served as the reducing agent for the synthesis of nanoparticles.
Synthesis of Iron Nanoparticles
Iron chloride hexahydrate (FeCl3 · 6H2O, analytical grade) supplied by Sigma-Aldrich (Sigma-Aldrich 220299) served as the precursor. 100 cc of distilled water was used to dissolve .027 g of iron chloride hexahydrate FeCl3 · 6H2O for preparing a solution of 1 mM concentration. The extract of Hibiscus rosasinensis flower was mixed in 1:1 ratio with 1 mm FeCl3 · 6H2O in an Erlenmeyer flask to synthesize iron nanoparticles. For an optimized reaction, the solution was thoroughly mixed at 250 rpm for an hour at room temperature. The solution was centrifuged at 6000 rpm for 15 minutes once the precipitate became visible. To get rid of all contaminants, the precipitate was rinsed with water and centrifuged in the same circumstances. To eliminate moisture and undesirable particles, the resultant pellet was overnight heated in a hot air oven at 48°C. A muffle furnace set to 405°C for 3 hours and was used to calcine the dried nanoparticle powder. For additional characterization and application research, nanoparticles were gathered and stored in a vial.
Materials and Methods
Preparation of Extract
Flowers from hibiscus plants have been collected. The stamens and calices have been stripped from the flowers, and their petals have been dried in the shade and ground into a fine powder with the help of a blender. The powdered petals have been stored in an airtight container at 4° C for future use and examination. A mixture comprising 5 g of hibiscus petal powder and 100 mL of distilled water has been prepared in an Erlenmeyer flask and subjected to heating at 60° C for one hour. After the mixture has been filtered using Whatman filter paper No. 1, the filtrate has been centrifuged for 15 minutes at 6000 rpm to remove tiny fragments. This extract has been used as a reducing agent for the synthesis of nanoparticles.
Synthesis of Iron Nanoparticles
Iron chloride hexahydrate (FeCl3 · 6H2O), of high purity and analytical grade, has been utilized and purchased from Sigma-Aldrich (Sigma-Aldrich 220299). A 1 mm FeCl3 · 6H2O solution has been prepared by dissolving 0.027 g of iron chloride hexahydrate in 100 cc of distilled water. This solution has been mixed in a 1:1 ratio with the extract of Hibiscus rosa-sinensis flowers in an Erlenmeyer flask for synthesizing iron nanoparticles. To optimize the reaction, the solution was thoroughly mixed at 250 rpm for one hour at room temperature. Once a precipitate has been formed, the solution has been centrifuged at 6000 rpm for 15 minutes. The
precipitate has been rinsed with water and centrifuged under the same conditions to eliminate
contaminants.
To remove moisture and undesirable particles, the resultant pellet has been heated overnight in a hot air oven at 48° C. The dried nanoparticle powder has been calcined in a muffle furnace set to 405° C for 3 hours. The synthesized nanoparticles have been collected and stored for future use.
Nanoparticle Characterization
UV-VIS Spectrophotometry (Model: G10S, Thermo Fisher Scientific Ltd., USA) has been employed for the initial confirmation of the synthesized Fe3O4
nanoparticles. The sample has been scanned to determine its absorbance maxima at wavelengths in the range of 200 to 700 nm.
The nature of the adsorbents on nanoparticles has been investigated using FTIR spectroscopy. An FTIR spectroscope has been employed to scan the samples at wavelengths between 8000 and 300 cm−1, displaying the results as a spectrum. The procedure involved mixing 150 mg of KBr and 0.5 mg of the sample.
An X-ray diffractometer (Xpert Pro, Belgium) has been used to assess the crystalline size of the nanoparticles. Using Debye-Scherrer’s equation, the size has been determined from the spectrum:
_Sample_1st_Pass-web-resources/image/Eqn001.png)
(1)
D: particle size
K: form factor
λ: wavelength
θ: Bragg’s angle
β: full width at half maximum
Batch tests for solutions
with a single metal ion
Stock solutions of Pb(NO3)2 and CuSO4 with a strength of 1000 mg/L have been prepared in distilled water. The solutions have been diluted as required, and analyses have been performed. The
analytical-grade chemicals have been purchased from Sigma-Aldrich.
Batch Tests for Solutions
with a Single Metal Ion
25.0 mL of Pb(II) and/or Cu(II) solution with an initial concentration ranging from 30 to 70 mg/L were diluted 50 mL using distilled water. Adsorption isotherms were determined by introducing 0.05 g iron oxide nanoparticles to respective 25.0 ml of metal ion solution. The pH of the solution has been adjusted to 5 ± 0.2. Samples have been collected at regular intervals of 5 minutes from 5–40 minutes. During all kinetic studies, the samples were vigorously shaken for 60 minutes at 200 rpm. After each time interval, the suspensions have been filtered through Whatman filter paper No. 42, and the quantity of heavy metals in the filtrate has been determined using an atomic absorption spectrophotometer.
The effect of pH has also been studied with the same value of metal ion solution and adsorbent. pH adjustments have been made using either 1M HCl or 1M NaOH.
Analysis of experimental data
Kinetic Model
Adsorption kinetics is a crucial factor in evaluating the effectiveness of the sorption process, as it defines the rate at which solutes are adsorbed and the residence time of adsorbates at the solid-liquid interface. The rate at which adsorption occurs is dependent on the number of particles adhering to the adsorbent surface per second and the frequency of particle collisions per unit area per second.
The impact of time on the sorption process is examined to determine the kinetic behavior of adsorbates on adsorbents. In this study, pseudo-first-
order and pseudo-second-order kinetic models have been used to analyze the impact of contact time on Pb and Cu adsorption by iron oxide nanoparticles. Adsorption data for adsorbates extracted from aqueous solutions are frequently analyzed using the pseudo-first-order model. According to this concept, the number of vacant binding sites on the adsorbent surface closely correlates with the adsorption rate.
Conversely, the pseudo-second-order kinetic model concentrates on adsorption processes that are controlled by interactions between adsorbates and the functional groups on the surface of the adsorbent that are mediated by chemical bonds. Based on equilibrium adsorption, this model takes into account that the amount of adsorbate adsorbed at equilibrium and the amount adsorbed at a specific time both influence the adsorption rate (Ho, Y. S., & McKay, G., 2000; Singh, S. et al., 2021).
The linear equation of pseudo-first-order kinetic equation is given below:
ln(qe − qt) = lnqe − k1t(2)
In eq. (2)
qe= amount adsorbed by nanoparticle at equilibrium by Pb and Cu in (mg/g),
qt = amount adsorbed at time t in (mg/g),
k1 = rate constant of first-order kinetics/minute.
qe, k1 and R2 were determined by straight line plots of log (qe− qt) against time.
The kinetic rate equation of pseudo-second-order kinetics is given by equation 3:
_Sample_1st_Pass-web-resources/image/Eqn003.png)
(3)
In eq. (3)
k2 = rate constant of pseudo-second-order kinetics (mg/g/min),
k2, qe, and R2 values were determined from the slope and intercept of the plots of t/qt against time.
Analysis of Pb(II) and Cu(II) adsorption
The eq. (4) and (5) are used to determine the amounts of Pb(II) and Cu(II) adsorbed by iron oxide
nanoparticles:
_Sample_1st_Pass-web-resources/image/Eqn004.png)
(4)
_Sample_1st_Pass-web-resources/image/Eqn005.png)
(5)
Ce = concentration of metal ions at equilibrium in (mg/L),
V = volume of solution of metal (L),
W = mass of the adsorbent (g), and
q = quantity of metal ions absorbed by iron oxide nanoparticles (mg/g). Experiments were carried out at room temperature.
Adsorption isotherm
Among the various adsorption isotherms, Langmuir and Freundlich’s isotherms are widely preferred for describing adsorption data across a broad spectrum of adsorbate concentrations. Langmuir adsorption isotherm assumes that homogeneous monolayer adsorption occurs at all surface sites, preventing interactions between adsorbed molecules and molecules at nearby adsorption sites (Ho & McKay, 2000).
Langmuir equation in linear form is given below.
_Sample_1st_Pass-web-resources/image/Eqn006.png)
(6)
In eq. (6),
qe = Amount of adsorbed metal ions in (mg/g),
Ce = Concentration at equilibrium in (mg/L).
qmax = Maximum adsorption,
b = Chemical affinity between adsorbent and adsorbate.
Freundlich isotherm equation is as follows:
qe = KF * C 1/n(7)
Eq. (5) in the logarithmic form is as follows
_Sample_1st_Pass-web-resources/image/Eqn008.png)
(8)
1/n and KF are isotherm constants respectively.
Results and Discussion
Nanoparticle Characterization
UV-Visible Spectroscopy
A typical transformation of the extract solution’s color of the solution of the extract from pinkish violet to black when the solution of iron salt was added to Hibiscus rosasinensis flower extract, served as preliminary confirmation for the generation of iron oxide nanoparticles (Latha, N. et al., 2014, Niraimathee,
V. A. et al., 2016). The absorbance spectra of the solution were measured across the wavelength range of 200 to 1000 nm. Fig. 1 displays the UV-VIS absorption spectra of the produced iron oxide. The highest absorbance was detected at 229 nm, with no peaks observed at 590 nm and 300 nm. These wavelengths correspond to the presence of various natural compounds in the extract. This suggests that during the synthesis of Fe3O4 nanoparticles, the hibiscus extract functions as both a reducing and capping agent.
_Sample_1st_Pass-web-resources/image/Figure_1.jpg)
Figure 1. UV-VIS spectrum of Iron Oxide nanoparticle.
FTIR Spectroscopy
FTIR analysis was carried out for IONPs and hibiscus extract to check for any possible modifications to the bonds between functional group atoms that might have happened during the process of reduction.
Fig. 2(a) shows the FTIR spectrum of Hibiscus rosa-sinensis extracts, while Fig. 2(b) depicts the FTIR spectrum of iron oxide nanoparticles synthesized using the same extract. Significant bands were observed at 1630 cm−¹ and 3396 cm−¹, corresponding to O–H bending and O–H stretching vibrations, respectively (Niraimathee, V. A. et al., 2016). These bands are primarily from the presence of water molecules in the sample. Additionally, multiple peaks corresponding to various organic functional groups were identified: C–H stretching at 2396 cm−¹, C=C stretching at 1630 cm−¹, and C–O–C bending and stretching at 1412 cm−¹ and 1062 cm−¹, respectively. These peaks confirm the presence of flavonoids, terpenoids, tannins, and organic acids in the flower extract (Niraimathee, et al., 2016 Lesiak et al., 2019).
In the case of the nanoparticles, the presence of Fe is evident from the prominent broad peak at 567.95 cm−¹ and an intense peak at 460 cm−¹, corresponding to the tetrahedral stretching and octahedral bending vibrations of Fe–O–Fe, respectively (Lesiak et al., 2019). These findings align with the UV-visible spectra discussed earlier and confirm the successful synthesis of the nanoparticles. It was found that the intensity of absorption bands was higher than that of the extract. It confirms the reducing action of the extract in the formation of iron oxide nanoparticles.
_Sample_1st_Pass-web-resources/image/Figure_2.jpg)
Figure 2. a. FTIR spectrum of Hibiscus Extract b. FTIR spectrum of Iron oxide nanoparticle.
XRD Analysis
To further ensure the formation of nanoparticles, XRD was conducted to determine the crystalline phases and crystallite size of the synthesized nanoparticles. By applying the Scherrer constant of 0.9 and utilizing the Debye-Scherrer equation, the average crystallite size was determined as 6.16 nm. The XRD pattern of the synthesized nanoparticles, shown in Fig. 3, exhibits multiple diffraction peaks corresponding to Fe3O4 at 2θ values of 29.2°, 35.4°, 43.1°, 53.4°, 57°, and 62.6°. These peaks correspond to the (220), (311), (400), (422), (511), and (440) Bragg reflection planes, respectively, as per JCPDS card 65-3107 (Shi et al., 2015).
These reflections confirm the formation of Fe3O4 nanoparticles. The presence of additional peaks may be attributed to residual impurities from the plant extract. However, these results are completely following the existing literature on Fe3O4 nanoparticles, which consistently report Fe3O4 diffraction planes within the 2θ range of 30°–60° (Hou, C. L. et al., 2013).
_Sample_1st_Pass-web-resources/image/Figure_3.jpg)
Figure 3. XRD image of iron oxide nanoparticle.
Role of Contact Time
The impact of time on removal on Pb (II) and Cu(II) was studied at different time intervals as shown in Fig. 4. Both Pb and Cu ions show a sharp increase in percentage removal within the first 5 minutes, achieving 82.86% removal for Pb and 76.9% removal for Cu, indicating rapid adsorption at the beginning of the process. After 10 minutes, the rate of removal slows down, and the curves plateau, suggesting that equilibrium has been reached. The maximum percentage removal is achieved within 30 minutes, with Pb (II) reaching 95.48% and Cu (II) reaching 84.9%. This can be a result of the adsorbent initially having a large surface area (physical adsorption). When the pores present over the adsorbent surface got clogged with heavy metal ions due to saturation on the binding site, the rate started to decrease towards a steady state. In this investigation, the contact time adsorption curve revealed three adsorption phases: the initial phase, which saw rapid heavy metal ion adsorption; the 2nd phase, which saw gradual slowing of the adsorption rate; and the equilibrium phase, which showed no discernible change in removal rate (Ho, Y. S. et al., 2000).
_Sample_1st_Pass-web-resources/image/Figure_4.jpg)
Figure 4. Effect of Contact Time on % Removal of Heavy metal ions.
Effect of pH
Several studies indicate that pH is a very important factor in determining the adsorption of heavy metals. The binding between the adsorbent and metal ions is often attributed to surface charges, functional groups, adsorbent solubility, and the degree of ionization.
From Fig. 5, it was observed the removal efficiency for both Pb (II) and Cu (II) increases proportionally with an increase of pH from 1 to 4, indicating that adsorption is favored at high pH value. At lower pH values (below 2), adsorption is relatively low, likely due to the high concentration of hydrogen ions (H+) competing with metal ions for adsorption sites. Pb (II) achieves maximum removal efficiency at a pH of approximately 5, with a removal percentage exceeding 95%. Similarly, Cu (II) reaches its maximum removal efficiency at a pH range of 4–5, with a removal percentage of about 85%.
Beyond pH 5–6, a slight decline in removal efficiency is observed for both metals, possibly due to the formation of metal hydroxides or precipitation at higher pH levels, which reduces the availability of free metal ions for adsorption. The optimal pH range for the effective removal of Pb (II) and Cu (II) is between 4 and 5. Consequently, pH 5 is selected as the optimum value for adsorption experiments. This finding emphasizes the importance of pH in optimizing adsorption processes for the removal of heavy metals from aqueous solutions.
_Sample_1st_Pass-web-resources/image/Figure_5.jpg)
Figure 5. Effect of pH on % Removal of Heavy metal.
Dose of Adsorbent
The percentage removal of Cu (II) and Pb (II) ions as a function of adsorbent dosage is shown in Fig. 6. The removal efficiency of both metals increases with adsorbent dosage, peaking at 40–50 mg, which can be attributed to higher number of adsorption sites available. Pb (II) achieves a maximum removal efficiency of 95%, while Cu (II) reaches 84.9%, indicating a stronger adsorption affinity of Pb (II) for the adsorbent. Beyond 40–50 mg, the removal efficiency
stabilizes, suggesting adsorption site saturation, where excess adsorbent does not significantly enhance removal. Based on this trend, 50 mg is considered the optimal adsorbent dose Additionally, Pb (II) consistently exhibits higher removal efficiency than Cu (II) at all doses, likely due to stronger bonding interactions between Pb (II) ions and the functional groups on the surface of iron oxide nanoparticles.
_Sample_1st_Pass-web-resources/image/Figure_6.jpg)
Figure 6. Effect of adsorbent doses on % Removal
Kinetic Model Analysis
The kinetic model elucidates the mechanisms and reaction pathways governing the adsorption of Pb(II) and Cu(II) over IONPs. It is possible to analyze the adsorption rate, describe the process efficiently, and identify the kind of adsorbent-adsorbate interactions—differentiating between physisorption and chemisorption—by fitting experimental data to several kinetic models. The adsorption kinetics are analyzed using both pseudo-first-order and pseudo-
second-order models, as shown in Fig. 7 and 8.
_Sample_1st_Pass-web-resources/image/Figure_7.jpg)
Figure 7. Pseudo-first order plot for Heavy metal ion removal at different time intervals
_Sample_1st_Pass-web-resources/image/Figure_8.jpg)
Figure 8. Pseudo-second order plot for Heavy metal ion removal at different time intervals
The pseudo-second-order models fit well the adsorption data much better (R² = 0.9998 for both Pb(II) and Cu(II)) compared to the pseudo-first-order, which has significantly lower R² values (0.3744 for Pb and 0.4051 for Cu). The pseudo-first-order model, which assumes physisorption via weak van der Waals forces, does not adequately describe the adsorption process. In contrast, the pseudo-second-order model, based on chemisorption involving covalent bonding or electron sharing, provides a near-perfect fit, confirming that chemisorption governs the adsorption mechanism. The pseudo-second-order rate constant (k2) is significantly higher than the pseudo-first-
order rate constant (k1), further supporting the dominance of chemisorption. Additionally, the pseudo-
second-order model predicts higher equilibrium adsorption capacities (qe) of 24.04 mg/g for Pb(II) and 21.23 mg/g for Cu(II) compared to the pseudo-
first-order model (qe = 15.86 mg/g for Pb(II) and 17.94 mg/g for Cu(II)). This confirms that the pseudo-
second-order model more accurately represents the actual adsorption capacity. Table 1 summarizes the kinetic parameters for Pb(II) and Cu(II) adsorption onto iron oxide nanoparticles.
Table 1. Kinetic Parameters for pseudo-first and pseudo-
second-order model.
|
Metal Ions |
|||
|
Kinetic Model |
Constants |
Pb |
Cu |
|
Pseudo-first order |
k1 |
0.0074 |
0.0078 |
|
qe |
15.86 |
17.94 |
|
|
R2 |
0.3744 |
0.4051 |
|
|
Pseudo-second order |
k2 |
0.1081 |
0.1541 |
|
qe |
24.04 |
21.23 |
|
|
R2 |
0.9998 |
0.9998 |
|
Adsorption Isotherm Model Analysis
Table 2 displays the Freundlich and Langmuir adsorption parameters, which were obtained from Langmuir isotherm (Fig. 9) and Freundlich isotherm (Fig. 10). Fig. 10 shows that Pb has a lower slope (0.0325) than Cu (0.0348), indicating that Pb has a slightly higher adsorption capacity. The maximum adsorption capacities of the nanoparticles for Pb and Cu were determined to be 30.77 mg/g and 28.74 mg/g, respectively. Additionally, Pb exhibits a higher Langmuir constant (b = 0.9503 L/mg) compared to Cu (b = 0.3997 L/mg), suggesting stronger adsorption affinity. The Freundlich model fits Cu adsorption very well (R2 = 0.9915) but does not fit Pb adsorption as well (R2 = 0.8176). The adsorption intensity values (n) for both Pb (3.58) and Cu (3.68) are greater than 1, indicating favorable adsorption and an increase in adsorption strength with concentration. Pb also has a higher Freundlich constant (KF = 15.81 mg/g) than Cu (KF = 11.81 mg/g), suggesting greater adsorption capacity in a heterogeneous system.
Table 2. Adsorption Parameters & Constant of Langmuir and Freundlich adsorption isotherm models
|
Metal ions |
|||
|
Adsorption Isotherm Model |
Constants |
Pb |
Cu |
|
Langmuir |
Qmax |
30.77 |
28.74 |
|
b |
0.9503 |
0.3997 |
|
|
R 2 |
0.9972 |
0.9973 |
|
|
Freundlich |
KF |
15.81 |
11.81 |
|
n |
3.584 |
3.6778 |
|
|
R 2 |
0.8176 |
0.9915 |
|
_Sample_1st_Pass-web-resources/image/Figure_9.jpg)
Figure 9. Langmuir Isotherm for Heavy metal ion removal at various concentrations
_Sample_1st_Pass-web-resources/image/Figure_10.jpg)
Figure 10. Freundlich Isotherm for Heavy metal ion removal at various concentrations
Overall, the Langmuir model provides a better fit for Pb adsorption (R2 = 0.9972 vs. 0.8176 for Freundlich), indicating that Pb adsorption is primarily monolayer. In contrast, the Freundlich model fits Cu adsorption well (R2 = 0.9915), though the Langmuir fit is also strong (R2 = 0.9973), suggesting that Cu adsorption may involve both monolayer and multilayer mechanisms. The iron oxide nanoparticles are more effective for Pb adsorption than for Cu, as Pb exhibits higher adsorption capacity and stronger affinity.
Conclusions
This study investigates the adsorption of Pb(II) and Cu(II) ions using iron oxide nanoparticles, focusing on adsorption kinetics, isotherms, pH dependence, and adsorbent dosage. Adsorption occurs rapidly, with over 80% removal within 5 minutes and equilibrium reached in 30 minutes. Maximum removal efficiencies are 95.48% for Pb(II) and 84.9% for Cu(II), following a pseudo-second-order kinetic model, indicating chemisorption.
Adsorption efficiency peaks at pH 5 for Pb(II) (95%) and pH 4–5 for Cu(II) (85%), decreasing at lower pH due to H+ competition and slightly declining beyond pH 5 due to metal hydroxide precipitation.
Increasing adsorbent dosage enhances removal, stabilizing beyond 40–50 mg due to site saturation. Pb(II) consistently shows a stronger adsorption affinity than Cu(II). Isotherm analysis identifies the Langmuir model as the best fit, with maximum adsorption capacities of 30.77 mg/g for Pb(II) and 28.74 mg/g for Cu(II).
The study highlights the cost-effectiveness and scalability of iron oxide nanoparticles for wastewater treatment, supporting global sustainability efforts.
However, its focus on Pb(II) and Cu(II) limits applicability to real-world wastewater systems with multiple contaminants. Key challenges include varying environmental conditions, competitive adsorption, regeneration potential, and industrial scalability. Future research should explore these factors to enhance the long-term viability of iron oxide nanoparticles for wastewater treatment.
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