Utilization of biosynthesized silica-supported iron oxide nanocomposites for the adsorptive removal of heavy metal ions from aqueous solutions

This study deals with heavy metal ions removal from simulated water using biosynthesized silica-supported iron oxide nanocomposites (nano-IOS). Agricultural and garden wastes have been utilized to prepare nano-IOS through a green synthesis process. Nano-IOS was characterized by XRD, SEM, FTIR, and zeta potential analysis. The nanocomposites were used to remove five heavy metals, viz., Pb2+, Cd2+, Ni2+, Cu2+, and Zn2+, with optimization of reaction parameters including pH, the concentration of heavy metals, adsorbent dosage, and contact time in batch mode experiments. The optimized dose of nano-IOS was 0.75 g/L for the adsorption of Pb2+, Cd2+, Ni2+, Cu2+, and Zn2+ (10.0 mg/L) with a contact duration of 70 min at pH 5.0 for Pb2+, Cd2+, and Cu2+ and 6.0 for Ni2+ and Zn2+. The adsorption behavior of the nano-adsorbent was well described by Langmuir adsorption isotherm and pseudo-second-order kinetic model indicating chemisorption on the surface of nano-IOS. The adsorption was also found spontaneous and endothermic. Thus, the environmentally benign and bio-synthesized nano-IOS can be utilized as an effective nano-adsorbent for the rapid sequestration of heavy metal ions from water and wastewater.


Introduction
The rapid increase in industrialization, on the one hand, fulfils the need for a rising population, but on the other hand, in addition to polluting water, industrial effluents from water systems pose a significant danger for the environment and natural ecosystems, as well as human health (Jain et al. 2018). Environmental contamination of groundwater with dangerous chemicals like dyes and metals has become a serious threat to the well-being of living organisms (Kulal and Badalamoole 2020). The heavy metals are non-biodegradable, carcinogenic, persistent, toxic and are widely distributed throughout the ecosystems. The food chain bioaccumulates heavy metals in living creatures, and their accumulation leads to various illnesses and malfunctions (Zhang et al. 2020). Metal resistance is the major issue since metals degrade and build up in living beings. Heavy ions are harmful to a large number of living forms; therefore, the presence of these ions is a major issue (Barakat 2011). Shortly, wastewater treatment will include several methods that take into consideration the removal of different pollutants (Fato et al. 2019). The uncontrolled release of pollutants has become a significant issue in recent years. Heavy metal ions tend to pose a significant danger to human health via bioconcentration, bioaccumulation, and biomagnification after entry into the food chain (El-Dib et al. 2020).
Several techniques have been developed over the years for the sequestration of metal ions from water, but adsorption 1 3 is the most operative technique for limiting the amount of water pollution caused by metallic species, dyes, surfactants, and other organic contaminants (Sebastian et al. 2018). The primary benefits of adsorption systems for water pollution management are their low initial cost and small footprint, as well as their simplicity of design and operation (Agasti 2021). Another benefit of this technique is the elimination of inorganic components, which is not possible with traditional biological treatment. The impurities present in water can be removed by using suitable absorbents, adsorbents, ionexchange resins, or chemicals (Joshi et al. 2020). The majority of adsorbents studied so far have not been thoroughly examined, making it impossible to evaluate their claimed effectiveness at the user end. Numerous investigated adsorbents lack critical characteristics such as user-friendliness in terms of readily available raw materials and methods of preparation, which is critical for commercializing the developed process, and universality in terms of removing heavy metals from multicomponent wastewater (Naseem and Durrani 2021;Oprčkal et al. 2017). As a result, there has been an increase in research towards finding less expensive alternatives to these adsorbents (Agasti 2021).
Nanomaterials such as metal oxides and their nanocomposites have proven quite effective in various industrial and environmental applications including wastewater remediation (Sharma et al. 2020). These are typically nano-sized adsorbents with high adsorption efficiency due to greater surface area. Nano-iron oxide has been recognized to be a biocompatible and highly efficient nano-adsorbent. Researchers have used Fe 3 O 4 and Fe 3 O 4 /activated carbon for Cu 2+ , Cd 2+ , and Cr 6+ removal (Jain et al. 2018); nanoiron oxide for adsorption of calcium and cadmium (Sebastian et al. 2018); nano-Fe 3 O 4 capped with CTAB for Cr 6+ (Elfeky et al. 2017); surfactant stabilized nano-Fe 3 O 4 for adsorption of lead, zinc, and cadmium (El-Dib et al. 2020); magnetite nanoparticles modified with graphene oxide for the removal of Cu 2+ ions (Danesh et al. 2021); ultrafine magnetite nanoparticles for the concurrent adsorptive removal of Cd 2+ , Cu 2+ , Ni 2+ , and Pb 2+ ions (Rao et al. 2007); nickel ferrite nanoparticles for adsorptive removal of Cr 6+ , Cd 2+ , and Pb 2+ (Khoso et al. 2021); and Fe 3 O 4 /montmorillonite nanocomposite for adsorption of nickel, lead, and copper (Kalantari et al. 2015).
Literature also reports the utilization of nanocomposites of iron oxide and silica, the conventional adsorbent (Neeli et al. 2020;Seifpanahi Shabani et al. 2017). With the rising approach of green chemistry, biogenic methods have been preferred to synthesize nano-scaled materials (Garg et al. 2021). The green synthesized nanomaterials are capped with functional groups present in the reducing medium that further improve their adsorption efficiency (Sebastian et al. 2018;Sharma et al. 2020). The various biological resources that have been reported for the synthesis of nano-adsorbents for the adsorptive removal of various heavy metal ions include coconut husk extract (Sebastian et al. 2018), gum ghatti (Kulal and Badalamoole, 2020), sewage sludge (Phuengprasop et al. 2011), cellulose biochar (Neeli et al. 2020), diatomite (Seifpanahi Shabani et al. 2017), tangerine peel extract (Ehrampoush et al. 2015), sugarcane bagasse (Buthiyappan et al. 2019;Jabasingh et al. 2018;Madhu et al. 2016), and henna leaf extract (Abid and Kadhim 2020).
This study discloses an economic and eco-friendly method that will help not only in the recycling of agricultural and garden waste into value-added nanocomposites of silica and iron oxide (nano-IOS) but also will provide an effective technique for adsorbing various heavy metal ions from aqueous medium (Sarwar et al. 2021). The study is also aimed to achieve high efficacy in terms of treatment volume within permitted limits. Experimental parameters that include pH, temperature, initial metal ion concentration, adsorbent dosage, and contact duration have been optimized for evaluating the adsorption effectiveness of the nano-IOS.

Materials
All the chemicals (A.R.) used in the study were procured from Merck, Mumbai. Deionized water was used to prepare all the solutions.

Preparation of nanocomposites
Nano-IOS were fabricated by modification of the already reported method (Sebastian et al. 2018). Sugarcane bagasse (agricultural waste) was obtained from local sugarcane juice vendors. The bagasse was cleaned by washing with tap water followed by dil. HCl and deionized water. It was dried out in the sunlight and powdered for further use. Twenty-five grams of powdered bagasse was boiled in the presence of 2 M NaOH solution for 3 h at 90 °C to obtain sodium silicate solution. Garden waste including leaves and flowers was washed with tap water and chopped to obtain smaller pieces. Twenty-five grams of the chopped parts were boiled in 100 mL of water for 1 h at 90 °C. The resulting solution was filtered to obtain the extract. A solution of 0.1 M iron (III) chloride was prepared and mixed with extract in a 1:1 ratio. Sodium silicate solution was added with constant stirring using a magnetic stirrer followed by the addition of a small amount of NaOH solution to obtain a basic solution. The solution was aged for 24 h. The obtained iron oxide-silica nanocomposites, nano-IOS, were centrifuged and rinsed with deionized water before being dried at 80 °C in a hot air oven before being stored for future usage as nano-adsorbents.

Characterization of nanocomposites
The morphological analysis of nano-IOS was executed by scanning electron microscope (Model Jeol JSM-6100). The functional groups in nano-IOS were characterized by FTIR (Model Perkin Elmer Spectrum 400) operating with a resolution of 2 cm −1 (range 4000-400 cm −1 ). The crystallinity of nano-IOS was analyzed through XRD (Model PANalytical X'Pert Pro) using Cu-Kα radiation (K = 1.5406 A°) operating at 45 kV and 2Ɵ ranging from 20 to 80° at a continuous speed of 0.045° per min. The zeta potential of nano-IOS was measured by Malvern zeta potential analyzer (Model Zetasizer Nano ZS90).

Adsorption of heavy metal ions
The solutions (10 mg/L) of Cu 2+ , Ni 2+ , Pb 2+ , Cd 2+ , and Zn 2+ were prepared by using deionized water, and further dilution was used to obtain the required concentration. During the batch experiments, a known amount of nano-IOS was added to 100 mL of the solution containing a known amount of heavy metal ions. A microprocessorbased pH meter (Model 1010 Labtronics) with an accuracy of ± 0.01 was used to monitor the pH of the solutions that was varied by adding the required amount of dil. HCl or dil. NaOH. The mixture was agitated and left for equilibration. Analysis was carried out by varying the temperature of the solutions from 288 to 328 K, the concentration of heavy metal ion solutions from 10 to 20 mg/L, pH from 2.0 to 7.0, dosage of nano-IOS from 0.1 to 1.25 g/L, and contact time from 10 to 100 min. The mixture was centrifuged and residual ion concentration in supernatant was determined by atomic absorption spectrophotometer (AAS, Model PerkinElmer PinAAcle 900 T).

Adsorption study
The percentage removal of heavy metal ions, %R, and the amount of adsorbate or adsorption capacity of nanocomposites at equilibrium, q e (mg/g), were calculated by Expressions (1) and (2), respectively: where m(g) is the mass of the nano-IOS; C i (mg/L) and C e (mg/L) are the initial concentration of heavy metal ions and equilibrium concentration of heavy metal ions respectively; and V (L) is the volume of the solution. (1) The adsorption process was further analyzed through the following approaches: (1) Langmuir adsorption isotherm with consideration of monolayer adsorption (2) Freundlich adsorption isotherm with consideration of multi-layer adsorption (3) Temkin isotherm equation with consideration of the uniform distribution of maximum binding energy between adsorbent and adsorbate. (4) Dubinin-Radushkevich isotherm with consideration of micropore volume filling instead of layered adsorption.
The thermodynamic properties of nano-IOS, including standard entropy change ΔS° (J/mol K), standard enthalpy change ΔH° (kJ/mol), and the standard Gibbs free energy change ΔG° (kJ/mol) for adsorption of metal ions by nano-IOS were calculated from the distribution coefficient of the systems, K (L/mg), using the following expression: The adsorption kinetics was analyzed by considering pseudo-first-order, pseudo-second-order, Elovich, and interparticle diffusion models.

Desorption study
The exhausted adsorbent was rinsed gently with deionized water to discard any unadsorbed metal ions and then agitated with 100 mL of 0.1 M HCl for the regeneration of nano-IOS. The solution was filtered, and the regenerated nano-IOS was washed with deionized water before drying and reuse in successive adsorption-desorption cycles. The concentration of metal ions in the desorbing solution was determined by AAS.
Desorption efficiency (%D) of metal ions was computed by the following expression: where C d (mg/L) and C a (mg/L) are the concentration of desorbed and sorbed metal ions, respectively.

Characterization of nano-IOS
XRD was used to analyze the crystalline nature of nano-IOS. Figure 1a represent the XRD pattern for nano-IOS with diffraction peaks at 2θ equal to 26.5° corresponding to the presence of silica, at 30.3°, 33.2°, 34.4°, 43.5°, 57.6°,, and 62.6° corresponding to inverse spinel form of iron oxide in consistence with other reports (Sarwar et al. 2021). The sharp peaks in the patterns can be attributed to the crystalline nature of the nano-IOS. The average crystalline size of nano-IOS was estimated by the Scherrer equation (Fan et al. 2017). The average particle size was found as 28.63 nm confirming the dimensions of nano-IOS in the nano-scale. The morphological analysis of nano-IOS was carried out using scanning electron microscopy (SEM). Figure 1b also shows the micrographs of nano-IOS with a heterogeneous matrix having roughly spherical particles with slight agglomeration. The particle size was found to vary from 20 to 50 nm. The pH at zero-point charge was estimated as 4.9.

Mechanism for the formation of nano-IOS
The leaves and flowers in the garden waste are enriched with phytochemicals containing polyhydroxy compounds such as polysaccharides, terpenoids, flavonoids, lignins, and saponins that play an important role in converting Fe 3+ ions to iron oxide nanoparticles as ferric ions are highly susceptible to oxidation (Sebastian et al. 2018). These nanoparticles can be subjected to silanization in the presence of sodium silicate solution in a basic medium (Allouche et al. 2014). The proposed mechanism is illustrated in Fig. 2 representing the green synthesis of nano-IOS. The nanocomposites with dimensions in the nano-scale have greater surface area and provide more active sites for the binding and adsorption of heavy metal ions in an aqueous solution (Sharma et al. 2020). Further, capping of the nanocomposites with various phytochemicals having polyhydroxy functional groups facilitate the binding of metal ions that has been explored through characterization techniques (Gong et al. 2012). FTIR was used to detect the functional groups present in nano-IOS as represented in Fig. 3a. The characteristic peak at 561.11 cm −1 was linked to stretching vibrations of Fe-O (Jain et al. 2018). Two sharp peaks at 1041.54 and 1087.33 cm −1 were credited to the stretching mode of the Si-O bond, while another peak at 879.79 cm −1 corresponded to the vibrating mode of Si-OH (Doermbach et al. 2014). Since there are various phytochemicals present in the plants that not only act as reductants but stabilizers for the nucleation reaction during the formation of nanocomposites, typical modes for various organic groups were observed. The characteristic band at 3331.02 cm −1 was attributed to O-H bending vibrations, while the medium peaks at 2970.26 cm −1 and 2937.57 cm −1 corresponded to stretching modes of asymmetric methylene (Sarwar et al. 2021). Furthermore, the peaks at 1736.21 cm −1 and 1658.57 cm −1 were credited to the stretching mode of C = O in the lignin aromatic group (Jain et al. 2018), while the peaks at 1458.34 cm −1 , 1413.39 cm −1 , 1380.70 cm −1 , and 1327.58 cm −1 were linked to C-H bending modes (Kulal and Badalamoole, 2020). A small peak at 1274.46 cm −1 was an indication of the epoxy C-O group (Danesh et al. 2021). However, the frequencies alter after adsorption of heavy metal ions as evident from Fig. 3b illustrating the shift in peaks reflecting adsorption of heavy metal ions due to binding with various functional groups present in nano-IOS (Danesh et al. 2021). The results indicate the active role of functional groups attached to the nano-IOS in binding with metal ions by plausible complexation or chemical reaction in consistence with earlier reported results (Elfeky et al. 2017).

Effect of process parameters on the removal efficiency of nano-IOS pH
The binding and adsorption of metal ions on solid surfaces are considerably affected by the pH of the solution with protonation and deprotonation of functional groups at the active sites (Ehrampoush et al. 2015). The optimum pH for adsorption of heavy metal ions was determined by studying the effect of pH (2.0-7.0) at a constant concentration of heavy metal ion (10.0 mg/L) and nano-IOS (0.1 g/L) for a contact time of 75 min at 298 K. The percentage removal by nano-IOS increased initially with an increase in pH from 2 and attained maximum value at specific pH with slight decrease afterward as shown in Fig. 4a. Adsorption is dependent upon the electrostatic forces between the adsorbent and adsorbate (Kulal and Badalamoole 2020). At pH < pH ZPC , the abundance of hydronium ions (low pH) causes competitive adsorption of positively charged metal ions and also leads to protonation of functional groups (carbonyl, hydroxy, amino and carboxylic groups) attached to the biosynthesized nano-IOS resulting in repulsive interactions and lesser adsorption of metal ions (Berg et al. 2009;Neeli et al. 2020). So the lesser removal percentage at low pH due to lesser adsorption of metal ions is possible because of the repellency among the positively charged metal ions and the surface of the nano-adsorbent. With increasing pH, i.e., pH > pH ZPC results in deprotonation of functional groups attached to the biosynthesized nano-IOS and increases the attractive interactions with metal ions leading to increased adsorption (Almomani et al. 2020). A similar trend was reported for the adsorption of heavy metal ions on nano-adsorbents (Jain et al. 2018). Thus, the optimum pH = 5.0 for Pb 2+ , Cd 2+ , , and Cu 2+ was obtained for the use of nano-IOS as adsorbent while pH = 6.0 was obtained for Ni 2+ and Zn 2+ . A slight decrease in the percentage removal of the metal ions beyond the optimum pH can be attributed to the precipitation of metal hydroxides (Igberase et al. 2017).

Adsorbent dosage
The effect of nano-IOS dosage was examined by varying the amount of nano-IOS from 0.1 to 1.25 g/L for the adsorption of heavy metal ions (10.0 mg/L) at optimized pH (5.0 for Pb 2+ , Cd 2+ , and Cu 2+ and 6.0 for Ni 2+ and Zn 2+ ) for a contact time of 75 min at 298 K and has been illustrated in Fig. 4b. A gradual increase in removal efficiency was noticed with increasing content of nano-IOS due to development in more active sites and gradually a plateau was attained (Khoso et al. 2021). However, at a specific concentration of nano-IOS as 0.75 g/L, adsorption equilibrium was established and no significant increase was obtained further. Thus, the optimal dosage of nano-IOS was considered as 0.75 g/L for use in further experiments. The increased efficiency of

Contact period and initial concentration of heavy metal ion
The effect of contact duration (10-100 min) on adsorption of heavy metal ions (10 mg/L) was studied by keeping other parameters constant (298 K, 0.75 g/L nano-IOS) at optimized pH and is illustrated in Fig. 4c. Initially, a progressive rise in the removal percentage of heavy metal ions is evident from the plots, which might be attributed to the initial  (Özsoy et al. 2007). However, an equilibrium/steady state is achieved after some time because of the occupancy of the active sites (Neeli et al. 2020). During the analysis, the adsorption capacity of nano-IOS increased to 10.65 mg/g from 7.58 mg/g with an increase in contact period up to 70 min that increased to 10.66 mg/g after 80 min and remained constant thereafter. Figure 4d depicts the effect of metal ion concentration (10-20 mg/L) under optimized adsorption conditions. The adsorption capacity increased with an increase in metal ion concentration for all the systems. The percentage removal was obtained as 79. 90, 82.83, 84.92, 87.00, and 87.99, respectively for Pb 2+ , Ni 2+ , Cd 2+ , Cu 2+ , and Zn 2+ after 70 min. An increased concentration of metal ions resulted in a slight decrease in metal removal percentage possibly due to the increasing covalent interactions as compared to the decreasing electrostatic interactions of metal ions with active sites (Gong et al. 2012). Furthermore, the fixed number of active sites with a constant concentration of nano-IOS results in unaffected occupancy and an overall decrease in removal percentage (Almomani et al. 2020).

Adsorption isotherms
The adsorption equilibrium for the sorption of Pb 2+ , Ni 2+ , Cd 2+ , Cu 2+ , and Zn 2+ was explored under optimized conditions through the adsorption isotherm models with isotherms plots illustrated in Fig. 5 and the computed adsorption parameters are listed in Table 1.
Langmuir isotherms are critical for gaining a better understanding of adsorption processes and the determination of the equilibrium adsorption capacity (Neeli et al. 2020). It is presumed that uniform adsorption occurs on the surface of the adsorbent with no interaction between the adsorbed species throughout the process (Sarwar et al. 2021). Additionally, the greater the value of the adsorption parameter K ads , the greater the affinity of the adsorbent for the adsorbate (Phuengprasop et al. 2011). According to the intercepts in Fig. 5a, the highest adsorption capacity of nano-IOS was observed for Zn 2+ followed by Cu 2+ , Cd 2+ , Ni 2+ , and Pb 2+ . The findings also indicate that the high binding affinity of nano-IOS is equivalent for Cu 2+ and Zn 2+ but lowest for Pb 2+ . The results can be attributed to the highest electronegativity of Zn 2+ and lowest of Pb 2+ among the studied metal ions. The more the electronegativity of the metal ion, the greater its binding with the functional groups of the nano-adsorbent and the higher its adsorption (Zhang et al. 2020). Furthermore, the high values of the correlation coefficient (R 2 ) values, close to unity (Table 1), suggest the efficiency of the model fit with the equilibrium data (Kulal and Badalamoole 2020).
The value of the parameter R L identifies the sort of isotherm as irreversible (R L = 0), unfavorable (R L > 1), linear (R L = 1), and favorable (0 < R L < 1) (Jain et al. 2018). In the  (Table 2), suggesting favorable adsorption onto nano-IOS under the applied process parameters. The maximum adsorption capacity q m of the heavy metal ions onto nano-IOS has been compared with that of other nano-adsorbents in Table 3 and confirm the suitability of nano-IOS as an efficient adsorbent. The adsorption data was further fitted to the linearized form of the Freundlich model, as evident in Fig. 5b, to explore the possibility of multilayer adsorption (Sebastian et al. 2018). This model considers the exponentiated distribution of active sites and the heterogeneity of the binding energies (Ehrampoush et al. 2015). The values of Freundlich constant and extent of adsorption were determined from the linearized Freundlich adsorption isotherms as illustrated in Fig. 5b. The obtained n values (Table 1) for the studied metal ions were found more than unity indicating favorable physisorption (Kulal and Badalamoole 2020). The varying values of n can be due to the varying extent of biosorption of various metal ions attributable to the preferential affinity of functional groups present at the active sites (Özsoy et al. 2007). The linearized plots for the Temkin isotherm model as evident in Fig. 5c were used to find the values of Temkin constant, b (kJ/mol). The corresponding values were found as less than unity reflecting a physisorption process (Seifpanahi Shabani et al. 2017). The values for mean free energy of adsorption, E DR , as obtained from the Dubinin-Radushkevich isotherms illustrated in Fig. 5d were below 8 kJ/mol indicating the adsorption process as physisorption (Zhang et al. 2020).
However, the Langmuir model was selected as the most suitable model for the description of the involved adsorption process because of the highest values of R 2 as compared to other models for all the studied systems. Thus, the adsorption of Pb 2+ , Ni 2+ , Cd 2+ , Cu 2+ , and Zn 2+ possibly is chemisorption and monolayer in nature (Xin et al. 2012). Literature also reports the monolayer binding of heavy metal ions on the surface of nanocomposites through chemisorption (Zhang et al. 2020). Figure 6a illustrates the effect of temperature (298-348 K) on the removal of heavy metal ions under optimized conditions. The increase in temperature resulted in enhanced adsorption of metal ions by nano-IOS (Ahmadi et al. 2014). The results reflect the adsorption of heavy metal ions by nano-IOS as an endothermal process (Jabasingh et al. 2018). Mobility of the metal ions increases with the increase in temperature, resulting in better interaction with the surface of nano-IOS leading to enhance adsorption (Barakat 2011). The linear plot of ln K against 1/T is illustrated in Fig. 6b with intercept equal to ΔS°/R and slope equal to − ΔH°/R. The spontaneity of the adsorption process is reflected by the negative values of ΔG° under the experimental circumstances (Danesh et al. 2021). Endothermic  Table 4). The positive values of ΔS° may be ascribed to an increase in randomness at the system interface with a high affinity of the nano-IOS for the metal ions (Seifpanahi Shabani et al. 2017). Thus, the thermodynamic study confirms that the adsorptive process on nano-IOS is endothermic and spontaneous.

Adsorption kinetics
The kinetics for the adsorptive process resulting in the removal of heavy metal ions (10 mg/L) by nano-IOS (0.75 g/L) was explored as a function of time (10-100 min) at 298 K and optimized pH. Figure 7 shows the fitted plots for the pseudo-first-order, pseudo-second-order, Elovich, and inter-particle diffusion models. The values of the kinetic parameters as determined from the respective plots for the models are listed in Table 5. The linear plots of pseudosecond-order demonstrate better fitness of the adsorption data when compared to the plots of other kinetic models (Kalantari et al. 2015). Furthermore, the plots for the Elovich model and inter-particle diffusion models show the difference in adsorption behavior with change in time. However, the non-crossing of fitting lines through origin omitted the suitability of these models for the studied systems. Furthermore, very high and close to unity value of R 2 verifies the validity of pseudo-second-order kinetics model to explain the adsorption kinetics. This model reflects the binding of adsorbate due to chemisorption and a direct relationship between the adsorption capacity of the adsorbent and the extent of occupied active sites on the adsorbent surface (Zhang et al. 2020). The model considers the monolayer formation on the adsorbent surface with rapid initial adsorption until equilibrium is attained (Khoso et al. 2021). A similarity in experimental and the calculated values of q e from pseudosecond-order model (Table 5) also confirms the suitability of pseudo-second-order kinetics model for the adsorption of heavy metal ions on the nano-IOS surface.

Desorption study
The regenerative efficiency of any adsorbent is significant for its reuse to compensate the cost associated with water treatment. The reusability of nano-IOS was examined in the successive adsorption-desorption cycles for the studied metal ions. The desorption efficiency was found more for Pb 2+ followed by Cd 2+ , Ni 2+ , Cu 2+ , and Zn 2+ possibly due to more effective binding of Zn 2+ with nano-IOS (Jain et al. 2018). The ease of regeneration of nano-IOS confirms the reversible nature of the adsorption of the studied metal ions onto nano-IOS. The regenerated nano-IOS exhibited good efficiency for successive seven adsorption-desorption cycles confirming the suitability as a cost-effective nano-adsorbent for the studied metal ions (Almomani et al. 2020).

Mechanism for adsorption
The adsorption of metal ions onto nano-adsorbents is a complex phenomenon with involvement of various interactions that are governed by physico-chemical properties of the metal ions as well as the functional groups present at the surface of the nano-adsorbents (Fan et al. 2021). The interactions may involve electrostatic interactions between the negatively charged surface of the nano-adsorbent and the positively charged metal ions (Garg et al. 2022), complexation of metal ions with the functional groups present at the surface of the nano-adsorbent and cation-exchange on the nano-adsorbent surface (Ren et al. 2018).  The participation of functional groups present at the surface of nano-IOS in the adsorption of metal ions is confirmed by the shift in the characteristic peaks of FTIR spectrum (Šoštarić et al. 2018). The various functional groups (carboxy, hydroxy, amino, and carbonyl) capping the nano-IOS bind the metal ions either due to complexation or due to proton-exchange. The increased adsorption of metal ions at pH > pH PZC also indicate the role of electrostatic interactions between the positively charged metal ions and the negatively charged surface of the nano-IOS (Rzig et al. 2021). The differential value of adsorption capacity of nano-IOS with varying electronegativity of the metal ions indicates the role of chemical interactions in the adsorption (Sarwar et al. 2021). The possibility of chemisorption as inferred by data fitting in Langmuir adsorption isotherm and the pseudo-secondorder kinetics is validated by the endothermic nature of the adsorption of the studied metal ions in the thermodynamic analysis (Esfandiar et al. 2022).

Conclusion
The study is directed to obtain nanocomposites of iron oxide (nano-IOS) in an easy, efficient, and economical way for further utilization for absorptive removal of various heavy metal ions, viz., Pb 2+ , Cd 2+ , Ni 2+ , Cu 2+ , and Zn 2+ in aqueous solution. The average particle size of the biosynthesized crystalline nano-IOS with roughly spherical morphology was obtained as 28.63 nm. The optimum process pH for adsorption of Pb 2+ , Cd 2+ , and Cu 2+ was obtained as 5.0, while pH = 6.0 was obtained for Ni 2+ and Zn 2+ . The best removal efficiency of nano-IOS was optimized at 0.75 g/L with a contact time of 75 min at 10 mg/L concentration of heavy metal ion at 25 °C. The high efficiency of the biosynthesized nano-IOS can be related to the capping by the functional groups in the extract that promote the binding of metal ions. The high values of regression coefficients for Langmuir isotherms suggested the adsorption of heavy metal ions by monolayer formation on the surface of nano-IOS. Furthermore, the better fit of the pseudo-second-order model due to the high values of the correlation coefficients confirmed the monolayer regime of adsorbate on the surface of adsorbent having the rate-limiting process of adsorption as chemisorption. Thus, under optimum process settings, the findings revealed substantial effectiveness of nano-IOS for the adsorptive removal of examined heavy metal ions.
Md. Amir Khan: Supervision, validation, data curation, writingoriginal draft. Table 5 Kinetics models parameters for adsorption of metal ions on nano-IOS** **q e (mg/g) and q t (mg/g) are the adsorption capacities of nanocomposites at equilibrium and at time t (min.) respectively, k 1 (min −1 ) is rate constant for pseudo-first order model, k 2 (g/mg min) is rate constant for pseudo-second order model, (mg/ g.min) is the initial adsorption rate, (g/mg) is the desorption constant, k id (mg/ g min. 1/2 ) is the rate constant for inter-particle diffusion, C