Pomegranate peel was sourced from a local food market in Giza, Egypt. The outer surface of the fruit was cleaned and rinsed with tap water. To prepare the extract, 50 g of dried (105˚C/2h) pomegranate peel was boiled in water for approximately 30 min in an Erlenmeyer flask. The resulting extract was then stored in a refrigerator at 4˚C [15][19]. To synthesize P-AgNPs, 10 mL of pomegranate peel extract (PPE) was added to a 0.01 M AgNO₃ solution. The mixture was stirred for 2 h at 70˚C to facilitate nanoparticle formation [13][21][22]. It has been reported that higher temperatures and concentrations can promote nanoparticle aggregation due to increased collision frequency among particles (Chekli et al., 2013) [23]. As shown in Figure 1, the color changed from pale yellow to dark, confirming the synthesis of P-AgNPs [22]. This color transformation is attributed to the reducing agent present in the extract [1][17][24].

Figure 1.Steps of synthesis of P-Ag-NPs using pomegranate peel.

Analytical grade lead nitrate (Pb(NO₃)₂), Nickel nitrate (Ni(NO₃)₂·6H₂O), cadmium nitrate tetrahydrate (Cd(NO₃)₂·4H₂O), and chromium nitrate pentahydrate Cr(NO₃)₃·9H₂O were purchased from Sigma Aldrich (≥99% purity). Stock solutions (1000 mg/L) of each heavy metal were prepared using deionized water and subsequently diluted to the required concentrations. All experiments were conducted at (25 ± 3)˚C with a shaking speed of 150 rpm. In each test, the adsorbent was placed into 1.0 L Erlenmeyer flasks, the mixture was agitated using a Thermo Scientific shaker, after the reaction phase, the heavy metal ion content was determined using an atomic absorption spectrometer (Thermo Fisher Scientific ICE 3000 AAS). The adsorption of Cd, Ni, Cr, and Pb onto P-AgNPs was evaluated at different contact times (0, 10, 20, 30, 40, 50, and 60 min) at 1.0 mg/L and 0.20 g. The effect of adsorbent dose was also investigated by adding varying amounts of P-AgNPs (0.10, 0.15, 0.20, 0.25, and 0.30 g) at 60 min, its optimum contact time, and 1.0 mg/L [25]-[27]. To assess the influence of pH on adsorption processes, the adsorption of Cd, Ni, Cr, and Pb onto P-AgNPs was studied at different pH levels (1.0, 3.0, 5.0, and 8.0) at 0.30 g and 60 min [11]. The adsorption experiments were performed using 100 mL working solution volume. The pH was adjusted using 0.1 M HCl and 0.1 M NaOH solutions. In addition, the synthesis procedure was revised to clearly indicate that 10 mL of pomegranate peel extract was mixed with 90 mL of 0.01 M AgNO₃ solution under continuous stirring at 70˚C for 2 h. The removal percentage (R%) was calculated using Equation (1) [27], where Ci and Ce represent concentrations at initial and equilibrium conditions, respectively.

All adsorption experiments were conducted in triplicate (n = 3). The reported values represent mean ± standard deviation (SD). Statistical analysis was performed using Excel sheet, and differences were considered statistically significant at p < 0.05.

The amount of metal ions adsorbed onto the adsorbent was determined using Equation (2) [28][29]. The adsorption equilibrium was analyzed using isotherm models, where the Langmuir isotherm model is represented by Equations (3) - (4), while the Freundlich isotherm model is described by Equation (5). Additionally, the adsorption kinetics of P-AgNPs were evaluated using Equations (6) - (7) [27][30].

where R% is the removal efficiency, qₑ is the amount of pollutants adsorbed per unit mass of adsorbent (mg/g), C₀ is the initial concentration (mg/L), Cₑ is the concentration after adsorption (mg/L), m is the mass of the adsorbent (g), and V is the volume of the solution (L), q_max is the highest amount of pollutants needed to produce a monolayer (mg/g). The Langmuir constants are denoted by q_max and K_L. The linear plot of C_e/q_e vs Cₑ can be used to calculate the values of q_m and K_L, 1/n = Intensity parameter and K is the Freundlich equilibrium constant (mg/g). Freundlich approach is represented by the Equation, which plots logq_e vs. logC_e linearly. q_t is the amount adsorbed at timet mg/g⁻¹ in t time (min), q_eq denotes the amount adsorbed at equilibrium, mg/g⁻¹ and k₁ is the pseudo-first-order constant (min⁻¹). Equation (7) and Equation (8) are not more than pseudo-first- and second-order adsorption kinetics. models. The q_eq and q_t are adsorption capacity at equilibrium and time, t, respectively. Also, K₁ and K₂ are pseudo-first and second-order kinetic constants, respectively.

UV-Vis spectroscopy is highly sensitive to the presence of silver nanoparticles (P-AgNPs). The maximum absorption band is observed at a wavelength of 386 nm, which is influenced by the size and shape of the nanoparticles. A UV-Vis absorption peak at 386 nm confirms successful AgNP synthesis because it matches the expected SPR signature of small, stable, and well-dispersed silver nanoparticles, indicating that metallic silver was formed and is present in the desired nanoscale state. As shown in Figure 2, the highest absorbance occurs at 386 nm in the UV-Vis spectrum. In the green synthesis process, Ag⁺ ions are reduced to Ag⁰ by electron-rich functional groups in pomegranate peel phytochemicals, particularly hydroxyl groups in phenols, flavonoids, and tannins. These compounds undergo oxidation to their corresponding oxidized derivatives while acting as both reducing and stabilizing agents for the silver nanoparticles, which, in turn, reduce Ag⁺ ions to Ag⁰. The mechanism of this reduction process is described by Equations (8) - (9).

Figure 2.The UV-Vis spectrum of P-AgNPs.

The XRD pattern of the synthesized P-AgNPs displayed distinct diffraction peaks at 2θ ≈ 38.11˚, 44.30˚, 64.44˚, and 77.3˚. As shown in Figure 3, these peaks can be indexed to the (111), (200), (220), and (311) crystallographic planes of face-centered cubic (fcc) metallic silver, in accordance with the Joint Committee on Powder Diffraction Standards (JCPDS Card No. 04-0783). The (111) plane at ~38.1˚: This is typically the most intense peak for fcc silver and indicates a high degree of crystallinity. A dominant (111) reflection often suggests Preferential crystal growth along this plane, which can enhance certain physicochemical properties such as catalytic activity. The (200) reflection at ~44.3˚ and (220) at ~64.4˚. The (311) peak at ~77.3˚: This further supports the complete formation of the crystalline silver phase, with no detectable peaks from silver oxide or other impurities. The absence of additional diffraction peaks confirms that the synthesized product is phase-pure metallic silver, with no crystalline contaminants. The sharpness of the peaks, despite nanoscale size, indicates a relatively high degree of crystallinity [9]. The XRD results clearly show that the Ag nanoparticles synthesized by the extract are crystalline in nature. The XRD results clearly show that the Ag nanoparticles synthesized by the extract are crystalline in nature.

Figure 3.The X-ray Diffraction (XRD) spectra of the prepared P-AgNPs nanoparticles.

The FT-IR spectra were used to identify the characteristic peaks of P-AgNPs, highlighting the role of biomolecules in pomegranate extracts in reducing silver ions to silver nanoparticles. Figure 4 presents the FT-IR spectra of P-AgNPs, confirming the presence of functional groups involved in the synthesis process. The broad band at 3406 cm⁻¹ corresponds to the O-H stretching vibration of hydrogen-bonded alcohols and phenols. The absorbance peak at 2900 cm⁻¹ indicates the presence of C-H stretching in alkenes. The band observed near 1600 cm⁻¹ is more consistent with C=C stretching (aromatic ring vibrations) or a conjugated C=O (carbonyl/amide I) mode rather than C-O or alkynic vibrations. C-O stretching vibrations typically appear in the 1000 - 1300 cm⁻¹ region, while alkyne (C≡C) stretches are expected around 2100 - 2260 cm⁻¹ and are often weak. C-N stretching modes occur broadly between ~1020 and 1360 cm⁻¹ and may overlap with C-O bands. Additionally, a sharp peak attributed to the C=O stretching vibration appears, followed by another peak at 1018 cm⁻¹. These functional groups confirm the successful synthesis and stabilization of P-AgNPs [31][32].

Figure 4.FT-IR spectrum of P-AgNPs.

To confirm that the silver particles are indeed in the nanoscale range, additional characterization using Transmission Electron Microscopy (TEM) is required. TEM offers high-resolution imaging that can directly visualize individual nanoparticles, allowing accurate determination of particle size distribution, shape, and degree of aggregation, thereby validating their nanoscale dimensions. As shown in Figure 5, TEM analysis revealed that the synthesized silver nanoparticles were predominantly spherical with a relatively narrow size distribution, ranging from (10 - 50) nm. The images showed well-dispersed particles with minimal aggregation, indicating effective stabilization by the phytochemical capping agents derived from pomegranate peel extract. The nanoscale dimensions observed are consistent with the surface plasmon resonance (SPR) peak at 386 nm in the UV-Vis spectrum and with the crystallite size estimated from XRD via the Debye-Scherrer equation. The clear, sharp particle boundaries in TEM micrographs further confirm the high crystallinity of the P-AgNPs, supporting the XRD findings of an fcc silver lattice. Occasional slight clustering of nanoparticles may be attributed to drying effects during TEM grid preparation rather than actual solution-phase aggregation. Overall, the TEM results validate the successful synthesis of small, stable, and well-dispersed P-AgNPs suitable for high-efficiency adsorption applications.

The EDX analysis confirmed the elemental composition of the P-AgNPs, revealing a strong peak at 3.6 keV, corresponding to silver (53%). The EDX spectrum of the synthesized Ag nanoparticles is presented in Figure 6. The presence of the Ag signal in the EDX spectrum confirms the successful synthesis of silver nanoparticles. The measured atomic ratio of C/Ag = 20.5/13.6 reflects the relative abundance of carbon (originating from the pomegranate peel phytochemicals used as reducing and capping agents) to metallic silver in the sample. The relatively high carbon content indicates that the AgNPs are coated with an organic layer rich in carbon-based functional groups (phenolic, hydroxyl, and carboxyl groups), which stabilize the nanoparticles, prevent aggregation, and may also contribute to their adsorption performance. These results validate the successful synthesis of P-AgNPs and the involvement of organic components from pomegranate extract in the nanoparticle formation [32][33].

Figure 5.EDX analysis of P-AgNPs.

Figure 6.The EDX analysis of P-AgNPs.

The removal efficiency of Pb, Ni, Cd, and Cr at different adsorption times (10, 20, 30, 40, 50, and 60 min) was observed to be 99.97%, 87.60%, 99.10%, and 11.00%, respectively. The highest removal efficiency (R%) was obtained at the optimal adsorption time of 60 min, as illustrated in Figure 7.

These results are consistent with findings from previous studies (Ahmed et al., 2023) [4], further validating the effectiveness of P-AgNPs in heavy metal adsorption.

The variation in removal efficiencies of P-AgNPs for Cr, Cd, Pb, and Ni can be attributed to the differences in their physicochemical properties, particularly ionic radius, hydration energy, and charge. Metals with a larger ionic radius and lower hydration energy, such as Pb²⁺ (ionic radius ≈ 1.19 Å), generally exhibit stronger adsorption due to easier dehydration and more effective interaction with active sites on the nanoparticle surface. In contrast, smaller ions with higher hydration energy, such as Ni²⁺ (ionic radius ≈ 0.69 Å), require more energy to shed their hydration shell, reducing their affinity for the adsorbent. Cr³⁺, having a higher charge (+3) and smaller radius (≈0.62 Å), tends to form strong electrostatic interactions, but its high hydration energy can limit adsorption unless surface sites are well-suited for trivalent ions. Cd²⁺, with an intermediate ionic radius (≈0.95 Å) and moderate hydration energy, showed removal efficiencies between those of Pb²⁺ and Ni²⁺ (Table 1). Therefore, the observed adsorption trend aligns with the interplay between ion size, charge density, and hydration energy, which governs the strength of metal-adsorbent interactions.

Table 1. The physicochemical properties of metal ions.

Figure 7.The removal efficiency of P-AgNPs for Cr, Cd, Pb, and Ni at different contact times.

The removal efficiencies of P-AgNPs at different adsorbent doses (0.10, 0.15, 0.20, 0.25, and 0.30 g/L) for Pb, Cd, Ni, and Cr were 100%, 99.02%, 96.50%, and 18.50%, respectively, as illustrated in Figure 8. The results indicate that the optimum P-AgNPs dose was 0.30 g/L. The increase in removal efficiency (R%) with higher adsorbent doses can be attributed to the availability of more vacant adsorption sites and free electrons, which enhance the degradation process. These findings align with previous studies (Sadon et al., 2012) [27], further supporting the effectiveness of P-AgNPs in heavy metal adsorption.

The observed increase in removal efficiency with higher adsorbent doses is attributed to the greater number of available active sites on the P-Ag-NP surface, which enhances the probability of metal ions interacting with and binding to these sites. At lower doses, many target ions remain unbound due to insufficient active site availability, resulting in lower removal efficiency. As the adsorbent dose increases, more ions are captured, and efficiency rises until a saturation point is reached. Beyond this optimal dose, further increases in adsorbent amount do not yield significant improvements because most of the available metal ions in solution have already been adsorbed. At this stage, active sites remain unoccupied simply due to the lack of remaining ions, and particle-particle aggregation at high doses may further reduce the effective surface area.

Figure 8.The removal efficiency of P-AgNPs for Cr, Cd, Pb, and Ni at different doses.

The adsorption efficiencies of Cr, Cd, Pb, and Ni at concentrations of 0.50, 1.0, 1.5, 2.0, and 2.5 mg/L using P-AgNPs were 100%, 99.99%, 99.67%, and 73.36%, respectively, as shown in Figure 9. These results highlight the exceptional capability of P-AgNPs for heavy metal removal, with Cr, Cd, and Pb achieving near-complete adsorption across all tested concentrations. The comparatively lower efficiency observed for Ni may be attributed to its smaller ionic radius and higher hydration energy, which limit its interaction with active sites on the nanoparticle surface.

Figure 9.The removal efficiency of P-AgNPs for Cr, Cd, Pb, and Ni at different concentrations.

The pH of the solution plays a critical role in the adsorption process because it influences both the surface charge of P-AgNPs and the speciation of metal ions in solution. At low pH values, the surface of P-AgNPs tends to be protonated, resulting in a net positive charge. This causes electrostatic repulsion between the adsorbent and positively charged metal ions (Cr³⁺, Cd²⁺, Pb²⁺, Ni²⁺), reducing adsorption efficiency. In addition, excess H⁺ ions compete with metal cations for active binding sites, further inhibiting metal uptake.

As pH increases toward a neutral or slightly alkaline range, deprotonation of functional groups (such as hydroxyl, carbonyl, and phenolic groups from the pomegranate peel phytochemicals coating the P-AgNPs) occurs, imparting a negative surface charge. This promotes electrostatic attraction between the negatively charged nanoparticle surface and the positively charged metal ions, enhancing adsorption efficiency. However, at excessively high pH values, some metal ions may precipitate as metal hydroxides rather than being adsorbed, complicating the adsorption mechanism. The interaction between P-AgNPs and metal ions during adsorption involves a combination of electrostatic attraction, surface complexation, and van der Waals forces. The phytochemical coating from pomegranate peel provides abundant electron-donating groups (-OH, -COOH, -C=O) that can chelate or coordinate with metal ions, while the nanoscale size of P-AgNPs offers a high surface area for binding. In some cases, weak physisorption may dominate, as indicated by pseudo-first-order kinetics, suggesting that the process is primarily governed by physical interactions rather than strong covalent bonding [28].

The highest removal efficiencies at an optimum pH of 6.0 were observed as follows: Cr (11.00%), Pb (99.97%), Ni (87.60%), and Cd (99.10%) as illustrated in Figure 10. The enhanced adsorption at pH 6.0 can be attributed to the increased availability of vacant sites for metal ion bio-sorption in an acidic medium. Additionally, the presence of negatively charged functional groups on the bio-sorbent surface at this pH facilitates stronger binding interactions with metal ions such as Cu, Pb, Se, Zn, and Cr, further enhancing adsorption performance [34]-[36].

Figure 10.The removal efficiency of P-AgNPs for Cr, Cd, Pb, and Ni at different pH values.

The adsorption study of metal ions onto P-AgNPs was conducted under optimum operating conditions: an adsorbent dose of 0.30 g, a contact time of 50 min, and a pH of 6.0. Figure 11 and Table 2 present the Langmuir and Freundlich isotherm model values obtained from the study. The adsorption data for Cr, Cd, Pb, and Ni showed a better fit to the Freundlich isotherm model than to the Langmuir model, as indicated by the relatively higher correlation coefficient (R²) values, which ranged from 0.8995 to 0.9256.

In adsorption studies, R² values closer to 1 indicate a stronger fit of the experimental data to the model, suggesting that the Freundlich equation adequately describes the adsorption behavior of the system. The superior fit to the Freundlich model implies that the adsorption occurs on a heterogeneous surface with sites of varying affinities, rather than on a uniform monolayer as assumed in the Langmuir model. This finding is consistent with the surface chemistry of P-AgNPs, where variations in surface functional groups, particle size, and morphology create a distribution of binding energies. In Table 2, the KF (Adsorption Capacity) = 694, a higher KF value indicates greater adsorption capacity, meaning P-AgNPs can remove a significant amount of metal ions from the solution. State that 1/n is approximately 0.25, and that this value indicates favorable adsorption on P-AgNPs, reflecting the nanoparticles’ heterogeneous nature.

Figure 11.The Langmuir and Freundlich isotherm models of P-AgNPs.

Table 2.Correlation coefficients (R²) of Langmuir and Freundlich models for the adsorption of metal ions into P-Ag-NPs.

Figure 12 and Table 3 compare the linear fits of the pseudo-first-order and pseudo-second-order kinetic models for the adsorption of Cr, Cd, Pb, and Ni onto P-AgNPs. The correlation coefficients (R²) obtained for the pseudo-first-order model were consistently higher than those for the pseudo-second-order model, indicating a better fit to the experimental data. This suggests that the adsorption process is primarily governed by a physical adsorption (physisorption) mechanism, where metal ions are retained on the nanoparticle surface through weak van der Waals forces and electrostatic interactions rather than through chemisorption involving strong covalent or ionic bonds [36].

Figure 12.The first and second kinetic models of Cd, Pb, Ni and Cr ions onto P-AgNPs.

Table 3.Correlation coefficients (R²) of the first and second kinetic models.

The regeneration and reusability of P-AgNPs were evaluated under the optimum adsorption conditions. Desorption of the adsorbed metal ions was carried out using 0.1 M HCl as the regenerating agent. After each adsorption-desorption cycle, the recovered adsorbent was thoroughly washed with deionized water, dried, and reused in subsequent adsorption experiments. The regeneration efficiency was assessed by comparing the adsorption capacity of the regenerated adsorbent with its initial adsorption capacity.

The results demonstrated a progressive decline in adsorption performance with repeated regeneration cycles. The regeneration results demonstrated a gradual decline in the removal efficiency of the biosynthesized AgNPs adsorbent over successive adsorption-desorption cycles. The removal efficiency decreased from approximately 95% in the first cycle to 83%, 54%, 39%, and finally 12% after the fifth cycle. This reduction in adsorption performance can be attributed to the progressive loss of active adsorption sites, partial structural deterioration of the adsorbent surface, and incomplete desorption of metal ions during the regeneration process. Furthermore, repeated exposure to adsorption and desorption conditions may lead to aggregation of silver nanoparticles and blockage of available binding sites, thereby reducing the adsorption capacity.

Despite the observed decline, the adsorbent maintained reasonable removal efficiency during the initial regeneration cycles, indicating its potential for reuse in wastewater treatment applications. However, the significant reduction in performance after multiple cycles suggests that further optimization of the regeneration procedure is required to improve the long-term stability and reusability of the adsorbent [37]-[40].

In adsorption studies, removal efficiency improves slightly during the first few regeneration cycles. This can be attributed to regeneration. Can clean the surface, remove weakly bound impurities, and expose additional active sites that were initially blocked. The regeneration agent may also enhance surface charge characteristics (protonation, deprotonation), improving the affinity toward target pollutants. Repeated contact with aqueous media can increase surface hydrophilicity, swelling, or pore accessibility, making adsorbate diffusion into active sites easier. Some functional groups become more reactive or better oriented. After initial adsorption-desorption cycles, strengthening interactions with pollutants. The increase in removal efficiency during early regeneration cycles is most likely due to surface conditioning, where repeated regeneration enhances accessibility and reactivity of adsorption sites before efficiency stabilizes. or declines with prolonged use.

The cost study of using P-AgNPs as adsorbents for removal of metal ions from an aqueous solution was calculated. The cost study for using P-AgNPs as adsorbents in metal ion removal from aqueous solutions was calculated and is presented in Table 4. The results indicate that the specific energy consumption for adsorbent production is 3.50 kWh/m³ with water consumption = 0.10 m³. The total cost required to produce 0.30 kg of the adsorbent is 11.50 Egyptian Pounds, equivalent to $0.23. This cost is significantly lower compared to other adsorbents, such as activated carbon (Ahmed et al. 2023) [4], and the adsorption process is environmentally friendly and does not generate secondary by-products. The cost-effectiveness of P-AgNPs as an adsorbent was evaluated by comparing both adsorption capacity and actual material cost with those of commonly reported adsorbents such as activated carbon, zeolite, and synthetic polymer-based resins. P-AgNPs exhibited an adsorption capacity of [506 mg/g for metal ions], which is higher than the reported values for activated carbon (50 - 90) mg/g (Mohammadi et al., 2010) [41] and natural zeolite (20 - 60) mg/g (Velarde et al., 2024) [42]. The production cost of P-AgNPs was estimated at $0.23 per 0.3 kg, primarily due to the use of pomegranate peel, an agricultural waste product, as the reducing and stabilizing agent. In contrast, the cost of commercial activated carbon ranges from $0.83 and that of ion-exchange Resins from $0.94. These results indicate that P-AgNPs combine high adsorption efficiency with low raw material costs, making them a competitive and sustainable alternative for heavy metal removal.

Table 4.The energy and material consumption for production of 0.30 kg of P-AgNPs.

*In Egypt, the cost of 1 m³ of water for industrial use = 10.0 L.E.; *In Egypt, the cost of 1 kWh of electricity for industrial use = 3.0 L.E.

The qₘₐₓ values are reported from the cited literature (Langmuir qₘₐₓ unless otherwise stated). Conditions (pH, T) (Qiaoqiao Su et al., 2021) [43] aim to close the gap between high-capacity but slow microporous ion-exchangers and fast, high-surface-area materials (CF/GO). MOFs and some carbon materials may show decreased stability or require harsh and important operational considerations. Advanced materials (MOFs, GO, tuned ACFs) can be costly but useful for high-value or small-volume treatments (Table 5).

In future work, we will validate the material under real wastewater conditions using grab and composite Influent/effluent samples from municipal and industrial sources and evaluation of regeneration/reuse over multiple cycles with mass balance of target pollutants. Analytical endpoints will include removal efficiency (COD/BOD, metals, dyes, pathogens as relevant), kinetics/isotherms in complex matrices. From these results, this study will guide process optimization and inform deployment pathways.

Uses non-toxic, renewable, and biodegradable materials (plant extracts, microbes, agricultural waste), making it suitable for large-scale sustainable production. Often relies on inexpensive natural precursors and mild reaction conditions (ambient temperature, aqueous medium), reducing operational costs compared to conventional chemical synthesis. The absence of harsh chemicals and high-energy requirements allows scaling up with lower environmental and energy footprints. Green synthesis can be adapted into existing bioreactors or batch/continuous-flow systems, which makes integration into industrial production feasible. Growing demand for environmentally friendly nanomaterials/adsorbents (in water treatment, catalysis, biomedicine) supports the scalability and commercialization of the method.

Table 5.Comparison study of P-AgNPs with other adsorbents for heavy-metal removal.

Plant extracts or microbial cultures may vary seasonally and geographically, leading to inconsistencies in nanoparticle size, shape, and yield. Lack of precise control over reaction parameters (pH, temperature, concentration of bioactive compounds) makes reproducibility difficult at an industrial scale. Removal of unreacted biomolecules or impurities can be difficult, time-consuming, and costly during scale-up. Some green methods produce lower yields compared to conventional chemical or physical synthesis routes.

Nanoparticles or bio-products synthesized by green methods may agglomerate or lose activity during storage, requiring stabilizers. Large-scale use of plant/microbial materials and their metabolites may face 448 regulatory hurdles (biosafety, quality control, toxicity testing).