The efficiency of nano-TiO2 and γ-Al2O3 in copper removal from aqueous solution by characterization and adsorption study

Water pollution is a major global challenge given the increasing growth in the industry and the human population. The present study aims to investigate the efficiency of TiO2 and γ-Al2O3 nanoadsorbents for removal of copper (Cu(II)) from aqueous solution as influenced by different chemical factors including pH, initial concentration, background electrolyte and, ionic strength. The batch adsorption experiment was performed according to standard experimental methods. Various isotherm models (Freundlich, Langmuir, Temkin, and Dubinin–Radushkevich) were fitted to the equilibrium data. According to geochemical modeling data, adsorption was a predominant mechanism for Cu(II) removal from aqueous solution. Calculated isotherm equations parameters were evidence of the physical adsorption mechanism of Cu(II) onto the surface of the nanoparticles. The Freundlich adsorption isotherm model could well fit the experimental equilibrium data at different pH values. The maximum monolayer adsorption capacity of TiO2 and γ-Al2O3 nanosorbents were found to 9288 and 3607 mg kg−1 at the highest pH value (pH 8) and the highest initial Cu(II) concentration (80 mg L−1) respectively. Copper )Cu(II) (removal efficiency with TiO2 and γ-Al2O3 nanoparticles increased by increasing pH. Copper )Cu(II) (adsorption deceased by increasing ionic strength. The maximum Cu(II) adsorption (4510 mg kg−1) with TiO2 nanoparticles was found at 0.01 M ionic strength in the presence of NaCl. Thermodynamic calculations show the adsorption of Cu(II) ions onto the nanoparticles was spontaneous in nature. Titanium oxide (TiO2) nanosorbents could, therefore, serve as an efficient and low-cost nanomaterial for the remediation of Cu(II) ions polluted aqueous solutions.


Materials and methods
Adsorbent. Nanostructured TiO 2 and γ-Al 2 O 3 were analytical reagent grade and employed without any further impurity (purity, 99%) from Nanopars Lima (www. Nanop ars Lima co, Iran). The morphology of the adsorbents was analyzed by scanning electron microscopy (SEM, S4800, Hitachi) coupled by energy X-ray dispersive (EDX) 24 . All scanning electron microscopy (SEM) images were recorded on a Hitachi S-4800 field-emission SEM microscope (Hitachi Ltd., Chiyoda, Tokyo, Japan). Transmission electron microscopy (TEM) analysis was carried out using a transmission electron microscope (model JEOL 2100F) operated at an accelerating voltage of 200 keV. The surface area and the average pore diameter of the adsorbents were determined using a Micro metrics ASAP 2010 gas adsorption surface analyzer at 77 K (Quantachrome Nova 2000e, USA) 17 . X-ray diffraction (XRD) was used for investigation the structure of the TiO 2 and γ-Al 2 O 3 nanoparticles using a X'Pert PRO MPD X-ray diffractometer (Panalytical, Almelo, Netherlands) equipped with Cu Kα radiation (λ = 1.5406 A o ) (40 kV, 40 mA) at scan rate at 3 s/step. Speciation. The speciation is a critical factor affects metal reactivity, including its solubility, adsorption, and precipitation behavior 5 . Determining of metal speciation helps to better understand metal behavior in soil and water 24 . A geochemical speciation model MINTEQ (Visual MINTEQ 3.1, KTH, Sweden; Stockholm) was used to calculate the various Cu species frequency and saturation indices in the aqueous solution with the highest Cu concentration (80 mg L −1 ). Visual MINTEQ is a geochemical equilibrium model extensively used for the accurate calculation of metal speciation, precipitation and solubility of dissolved mineral phase in aqueous solution 4 . The saturation index (SI) is calculated from the difference between the logarithm of the ion activity product (log IAP) and, the logarithm of the temperature corrected solubility constant (log K s ) for each solid compound of the experimentn 25 . Over-saturation, under-saturation and equilibrium conditions with the solid phase are occurred when SI > 0, SI < 0, and SI = 0 (or more accurately, −0.5 < SI < 0.5) respectively 25 . Adsorption isotherms. Batch sorption experiment was performed at room temperature (25 ± 1 °C) in conical flasks by stirring a mass of 0.05 g nanoadsorbents (TiO 2 and γ-Al 2 O 3 separately) with 5 mL of Cu(II) solutions at different initial concentrations (0, 2.5, 5, 10, 20, 40, and 80 mg L −1 ) for 2 h at 1000 rpm and left for 4 h at room temperature for equilibration. In order to separate of solid-liquid phases of nanoparticle suspensions, the solutions were centrifuged for 30 min (1000 rpm) and filtered through No. 42 Whatman filter paper 26 . The filtrate was analyzed by Flame Atomic Absorption Spectrophotometer (AAS, Shimadzu AA-6300, Japan) at a wavelength of 325 nm. These initial concentrations were selected based on a concentration range frequently existed in contaminated waters 27 . The stock solution of Cu(II) was prepared by dissolving CuSO 4 ·5H 2 O (Merck Co.) in deionized water to the concentration of 1 g L −1 . The experiment solutions were prepared by diluting the Cu(II) stock solution in accurate proportions to needed initial concentrations 28 . The background solution was 0.01 M calcium chloride (CaCl 2 ) to the neutralization of ionic strength 2 .
The effect of pH on the Cu(II) adsorption was studied over the pH range from 6.0 to 8.0 with γ-Al 2 O 3 and 4.0 to 8.0 with TiO 2 nanoparticles. The pH of Cu(II) solution was adjusted by using either 0. www.nature.com/scientificreports/ NaOH. Each experiment was duplicated under identical conditions with less than 5% standard deviation. The adsorption capacity of the adsorbents at equilibrium was calculated by the following equation 17 : where Q e is the amount of metal ion adsorbed (mg kg −1 ), C 0 and C e (mg L −1 ) are the initial and equilibrium concentration of metal ion solution, respectively. V is the volume of adsorbate in liter and m is the amount of adsorbent in grams. The formula for calculating the removal efficiency of the adsorbents was expressed by Eq. (2) 29 : where C 0 (mg L −1 ) and C e (mg L −1 ) are the initial and equilibrium Cu(II) concentrations respectively. Equilibrium data are basic requirements to understand the mechanism of the adsorption. Classical adsorption isotherm models, Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich (D-R), are used to describe the equilibrium between adsorbed Cu(II) on the adsorbents (q e ) and Cu(II) concentration in solution (C e ) at a constant temperature 11 .
The Langmuir adsorption isotherm assumes that adsorption occurs at specific homogeneous sites within the adsorbent and has found successful application in many monolayer adsorption process 30 . The non-linear form of the Langmuir isotherm equation is computed by using the following expression 31 : where Q m and C e have the same definitions as in Eq. (3), K L is a constant of the Langmuir equation, that is related to adsorption affiliation of binding sites for ion adsorption (L g −1 ), and S m is the maximum adsorption capacity with monolayer coverage (mg kg −1 ) 32 .
The effect of isotherm shape has been discussed to predict whether an adsorption system is favorable or unfavorable 3 . The essential feature of the Langmuir isotherm can be expressed by means of 'R L ' , a dimensionless constant referred to as separation factor or equilibrium parameter R L is calculated using the following equation 18 : where K L is the Langmuir constant (dm 3 mol −1 ) and C 0 the highest initial Cu(II) concentration (mol dm -3 ). The dimensionless Langmuir constant, or equilibrium parameter, (R L ) indicates if the isotherm is irreversible (R L = 0), favorable (0 < R L < 1), linear (R L = 1), and unfavorable (R L > 1) 33 .
The Freundlich empirical equation is employed to describe heterogeneous systems. It elucidates physical adsorption on surfaces (homogenous and heterogeneous). A non-linear form of the Freundlich equation is expressed as 34 : where Q m is the amount of metal ion adsorbed (mg kg −1 ), K F is the Freundlich constant representing the adsorption capacity (mg g −1 ), C e is metal equilibrium concentration (mg L −1 ), and the adsorption intensity was expressed by n as a constant value (dimensionless) 34 .
Temkin isotherm model assumes that the adsorption energy decreases linearly with the surface coverage due to adsorbent-adsorbate interactions. The linear isotherm equation is expressed as 18 : where Q m and C e are the same as above mentioned, A is the constant and intercept of equation (Lg −1 ), and K T is the constant value of the Temkin equation that is represented the sorption heat (J mol −1 ) 35 . The value of heat of adsorption (K T ) less than 40 kJ mol −1 indicates a physical adsorption and more than 40 kJ mol −1 represents chemical adsorption 36 .
The Dubinin-Radushkevich (D-R) isotherm is more general than the Langmuir isotherm because it does not assume a homogeneous surface or constant adsorption potential 9 . It was applied to distinguish between the physical and chemical adsorption of Cu(II) ions 9 . The non-linear form of (D-R) isotherm equation is expressed as 37 : where Q m is the adsorbed ion amount per unit weight (mmol g −1 ), q DR (mmol g −1 ) and β DR (mol 2 J -2 ) are the empirical constants of the equation and ε DR is related to the Polanyi potential that expressed as RT ln (1 + (1/C e )), where R and T are the gas constant (8.314 J mol −1 K −1 ) and absolute temperature (K) respectively 37 . The adsorption free energy (E) generally is related to the value of β DR that can be computed from the following equation 37 : The type of adsorption mechanism is related to adsorption free energy (kJ mol −1 ) 37    www.nature.com/scientificreports/ verify any changes which were statistically negligible. The suspensions were shaken for 2 h and left overnight for equilibration 38 . Equilibrium Cu(II) concentration in solution samples were measured by Flame Atomic Absorption Spectrophotometer, after centrifuging at 10,000 rpm for 30 min.
Thermodynamics. In any adsorption procedure, values of thermodynamic parameters such as Gibb's free energy (ΔG°), standard enthalpy (ΔH 0 ) and entropy (ΔS 0 ) must be taken into consideration in order to determine the thermodynamic nature of a process 18 . Values of thermodynamic parameters are the actual indicators for practical application of a process 26 . Adsorption of Cu(II) onto nanoadsorbents base on thermodynamic studies was calculated at a range of temperature (298, 303, 308, and 313 K). The Gibb's free energy (ΔG°) was calculated by the following equation 18 .
where R, T and K c are commonly gas constant (8.314 J mol −1 K −1 ), absolute temperature (K), and the equilibrium adsorption constant, respectively 11 . The Arrhenius equation was used to determine kinetic of adsorption reactions. It can be expressed as the following equation 11 : where K B is the Boltzmann constant (1.380 × 10 -23 J K −1 ), T is the absolute temperature (K), h is the plank constant (6.626 × 10 -34 J S −1 ), C 0 is the concentration (mol L −1 ), R is the gas constant (8.314 J mol −1 K −1 ), and ΔG° is Gibb's free energy (KJ mol −1 ) respectively 11 . Standard enthalpy (ΔH 0 ) and entropy (ΔS 0 ) were determined from the Van't Hoff isotherm equation as follow 11 : ΔH° and ΔS° were obtained from the slope and intercept of the plot of ln K vs 1/T.

Statistics.
The coefficient of determination (R 2 ) and the root mean square error (RMSE) statistics were used to evaluate the goodness of fit and absolute error measures respectively. The RMSE is expressed as 39 : where P i and O i are the predicted and measured values of Cu(II) concentrations sorbed to nanoadsorbents and n is the number of initial Cu(II) concentrations applied in sorption experiment respectively 30 . Root mean square error (0 to + ∞) was used as an index of absolute error. A lower RMSE and higher R 2 values show better goodness of fit between measured and estimated data 30 . Statistical evaluation was performed using statistical analysis software (SAS 9.4; Institute, 2011), speciation of Cu(II) in aqueous was accomplished by Visual MINTEQ 3.1, and bar chart, and line graphs were drawn using Microsoft Office Excel 2015 software. Optimization of various parameters of adsorption models was performed using Solver 2015.
Complying with relevant institutional, national, and international guidelines and legislation. The authors declare that all relevant institutional, national, and international guidelines and legislation were respected.

Results
Characterization. The morphological features and crystal structure of the nanoparticles obtained from SEM-EDX analysis is provided in Fig. 1. The nanoparticles showed a rough sphere-like structure before adsorption. Although γ-Al 2 O 3 nanosorbents showed a high surface homogeneity, TiO 2 nanoparticles had an irregular structure, thus makes possible the adsorption of Cu(II) ions on different parts of the adsorbent (Fig. 1). The mean diameters of TiO 2 and γ-Al 2 O 3 nanoparticles were 20 nm and 200 nm respectively based on TEM analysis (Fig. 2).
Energy dispersive X-ray spectroscopy (EDX) analysis was used to characterize the elemental composition of the nanoTiO 2 and γ-Al 2 O 3 . The results are summarized in Table 1. It can be found from the EDX analysis that titanium (Ti) and oxygen (O) are the main elements presented in the nano-TiO 2 , which confirms the formation of TiO 2 . The EDX data shows that other elements percentage were very negligible in TiO 2 nanoparticles. Same results were found in γ-Al 2 O 3 nanoparticles (Table 1), which mainly consists of aluminium (Al) and O. The molecular ratio of Ti: O (nano TiO 2 ) and Al: O (nano γ-Al 2 O 3 ) of the nanoparticles, calculated from EDX and quantitative analysis data, is close to that of bulk, which again confirmed that the grown nanoparticles are pure.
Structural information of the final product could be given by powder X-ray diffraction (XRD) analysis. In the XRD pattern of sample (Fig. 3), all the observed peaks can be indexed to a pure tetragonal anatase phase (JCPDS card, 21-1272) and aluminum oxide. No peak of other phases was observed, which indicates that the products are pure and well crystallized.    www.nature.com/scientificreports/  www.nature.com/scientificreports/ Effect of pH on adsorption. pH is an essential parameter of adsorption study which affects the sorption capacity of the nano-adsorbents and regulates the feasibility of treatment method. The effect of pH on Cu(II) adsorption on the nanoparticles is shown in Fig. 6. The results indicate that the TiO 2 nanoadsorbents have a greater adsorption capacity than γ-Al 2 O 3 for all pH values from 4.0 to 8.0. Copper (Cu(II)) adsorption significantly increased by increasing of pH values for both nanoparticles (Fig. 6).
Similarly, according to Li et al. 40 , Cu(II) reached its highest adsorption on functionalized bentonite at pH > 6.5. This adsorption exhibited a dependence on the electrostatic interactions related to the presence of functional groups. The heavy metals adsorption, as determined in several studies, is favored at moderate pH values than at lower pH values. For instance, nanofiber chitosan/TiO 2 chelated Cu(II) ions with minimum removal at 2-4 pH values and maximum removal at more than 6 pH values 31 .
Adsorption is a time-dependent processes. The adsorption kinetics of Cu(II) with the adsorbents occurred rapidly and reached the equilibrium after 4 h (data not shown) in this study. Therefore, a contact time of 4 h was selected for all batch adsorption experiments in order to ensure that the equilibrium was established. Although the kinetic study was not reported, the results of previous researches of Cu(II) adsorption kinetics on TiO 2 and γ-Al 2 O 3 nanoparticles demonstrated that the Cu(II) adsorption by TiO 2 occurred rapidly and reaching adsorption equilibrium after 5 h and the pseudo-second order equation described the kinetic data well as the predominant mechanism 41 . Meanwhile previous researches 42 demonstrated that adsorption kinetics were the best fitting by a pseudo-second order kinetic model. This model is more likely to predict the kinetic behavior of sorption, with  www.nature.com/scientificreports/ chemical sorption being the rate-determining step. Previous researches 43 reported that the pseudo-second order kinetic equation, which relies on the assumption that the chemical reaction might be considered as the ratecontrolling step, is the better model in the studied adsorption systems. In this process, the metal ions join the adsorbent surface by forming a chemical bond through sharing or exchange of electrons and tend to find sites that maximize their coordination number with the surface. Generally, a continuous multi-step process may be taking place during the sorption of a sorbate by a porous sorbent.
To determine the maximum Cu(II) adsorption and adsorption parameters, four equilibrium models were fitted to the experimental data, including the Freundlich, Langmuir, Temkin, and Dubinin Radushkevich equations. Table 3 represents the calculated values of isotherm parameters. Comparing of the statistical parameters listed in Tables 3 and 4, indicated that the Freundlich isotherm was the best to predict the equilibrium adsorption behavior. This indicated the surface heterogeneity of nanoparticles, uniform energy distribution, and reversible Cu(II) adsorption during the sorption process. The maximum adsorption capacities (S m ) and the Langmuir equation constant parameter (K L ) were equal to 9288 and 3607 mg kg −1 and 0.78 and 0.11 L mg −1 for Cu(II) adsorption on TiO 2 and γ-Al 2 O 3 nanoparticles at the highest pH value respectively, which decreased by decreasing of pH (Table 3). The adsorption capacity of TiO 2 nanoparticles were 2.5 times more than γ-Al 2 O 3 , which indicated TiO 2 nanosorbents were more efficient for Cu(II) removal than γ-Al 2 O 3 nanoparticles. Comparing of the results with various previous studies shows that experimental data of the present study was found to be lower than some recently reported studies 26 . The higher values of K L confirmed the ease of adsorption processes 44,45 .
All the values of R L lie between 0.09 and 0.30 for all studied pH values, indicating favorable adsorption of Cu (II) onto the nanoparticles (R L < 1). The removal efficiency kept on decreasing and reached a constant value with increasing of initial concentration. Same trend was found with γ-Al 2 O 3 nanoparticles. Hence, initial concentrations of 10 and 5 mg L −1 were taken as the maximum value for Cu(II) removal with TiO 2 and γ-Al 2 O 3 nanoparticles respectively (Fig. 7). From Fig. 7a it seems that at higher concentration the removal efficiency suddenly decreases. It was clearly interpreted that desorption occurs at higher concentration. This means the adsorbent is not stable and was not feasible for further usage.
Ionic strength and background electrolyte. Different adsorbates compete for adsorption sites characterized by maximum heat of adsorption and minimum free energy of adsorption 46 . The competitive effect of some ions (Ca 2+ and Na + ) on Cu(II) adsorption with TiO 2 and γ-Al 2 O 3 nanoparticles was investigated in the presence of CaCl 2 and NaCl as various background electrolytes at different ionic strengths (0.01, 0.1, and 0.5 M) ( Table 5). Ranging of 0.01 to 0.5 M ionic strength caused to decrease of Cu(II) adsorption with TiO 2 nanoparticles. More decreasing of Cu(II) adsorption was found in the presence of CaCl 2 than NaCl, which may be due to the higher competition of Ca 2+ ions with Cu(II) for adsorption on active surface sites 44 . The results of previous researches 7 showed the significant decrease of Cu(II) adsorption using various nanoparticles by increasing of Ca 2+ concentration in solution. For γ-Al 2 O 3 , the behavior was similar, however, more Cu(II) adsorption was found with TiO 2 than γ-Al 2 O 3 nanosorbents in the constant ionic strength and initial concentration (Table 5).
Thermodynamic. The thermodynamic parameters were determined for feasibility and spontaneity of the adsorption process at different pH values. The Gibbs's free energy (ΔG 0 ) and ΔH° and ΔS° have been calculated using thermodynamic relationship (Table 6).

Discussion
Based on the International Union of Pure and Applied Chemistry (IUPAC), the TiO 2 and γ-Al 2 O 3 nanoparticles consists of micropores with diameter less than 20˚ A. The morphological properties of the nanoparticles surface are suitable for adsorption processes. The specific surface area was measured by BET equation. The surface area of TiO 2 nanoparticles (200 m 2 g −1 ) was more than γ-Al 2 O 3 (150 m 2 g −1 ), which can influence the adsorption capacity of the nanomaterials 47 . Previous researchers demonstrated that increasing of surface area and decreasing of nanoparticles diameter caused to increase of adsorption capacity 27 . www.nature.com/scientificreports/ Several factors can influence the chemical behavior, biotoxicity, and bioavailability, and ultimately chemical fate of either nutrients or heavy metals in the environment 48 . Solution pH is a critical factor that affect the distribution of absorbable species of heavy metals 30 . Besides, the adsorption mechanism (surface precipitation vs. adsorption) can affected by pH. Based on the obtained results, no precipitation was found in solution with the nanoparticles (Table 2) and the physical adsorption on nanoparticle was accrue. It can be inferred that the increase of pH results in less H + available to compete with Cu(II) and/or Cu(OH) + for the same adsorption sites on the surface of the adsorbent. Furthermore, as the pH increases, Cu(II) will hydrolyze to Cu(OH) + , which is the species most readily adsorbed 49 . Increasing of Cu(II) adsorption with nano-oxides was attributed to the pH-dependent charge of these adsorbents 4 . Meanwhile, electrostatic repulsion between positively charge Cu(II) ions and positively nanoparticle surfaces generated at the pH less than pH ZPC , causes to diminish the adsorption of Cu ions as outer sphere complexes 50 . However, inner sphere complexes are responsible for metal adsorption at pH > pH ZPC . As can be seen in Fig. 6. b, more Cu(II) adsorption on TiO 2 nanoadsorbents was occurred at pH values above 6.0 as the pH ZPC of the TiO 2 nanoparticles, due to deprotonation of hydroxyl groups on nanoparticle surface and increase of electrostatic forces between Cu(II) ions and negatively surface charges 30 . Decreasing of Cu(II) adsorption at pH 4.0 is due to electrostatic repulsive force between Cu(II) ions and positively surface charge of the nanoadsorbent, resulted from protonation of surface functional groups. Though, at low pH the protonated active sites numbers increase and caused a great repulsion with positive charged toxic pollutants www.nature.com/scientificreports/ that greatly reduces the adsorption capacity of the nanoadsorbent 31 . At very high pH values, several complexes between metal species and OH groups formed that blocked the large numbers of adsorbent active sites and reduced their adsorption capacity 51 . Previous researches showed that Cu(II) adsorption on γ-Fe 2 O 3 nanoparticles was maximum at pH 7 value 52 . Same result was found by Huang et al. 53 , who found that the Cu(II) adsorption reached its highest at pH values more than 6.5. Although the same adsorption trend was obtained with both nanoparticles, the adsorption capacity of TiO 2 was more than γ-Al 2 O 3. Previous researches demonstrated the high adsorption capacity of TiO 2 for removal of heavy metals from aqueous solutions 27 . As shown in Fig. 6.a, The Cu adsorption was reached to the maximum value at pH values more than 7.2 (pH ZPC, ) 1 . The most abundant Cu species in solution were Cu(OH) 2 and Cu(OH) + at pH above 6.0 (Fig. 4). Same results were reported by other researchers 8 .
The values of n parameter from the Freundlich isotherm for the adsorption of Cu(II) by TiO 2 and γ-Al 2 O 3 nanoparticles were all greater than 1 at various pH values, indicating Cu(II) ions adsorption on the adsorbent surface were favorable 3 . The correlation coefficient of the Temkin isotherm equation is small, which indicates that the adsorption process of Cu(II) by the nanoparticles is not suitable for description by the Temkin isotherm model ( Table 3). The Dubinin Radushkevich equation was fitted to specify the chemical or physical adsorption mechanisms. The adsorption free energy of the Dubinin Radushkevich equation was less than 8 kJ mol −1 with both nanoparticles at different pH which is evidence of physically adsorption mechanism of Cu(II) onto nanoparticle surface (Table 3). In physical adsorption, the individuality of the adsorbate and the adsorbent are preserved. In return, chemisorption occurs as a chemical reaction between the adsorbate and the surface. Also, new chemical bonds are generated at the adsorbent surface 35 .
At the initial stage of the adsorption process and low Cu(II) concentration, there were free surface adsorption sites on nanoparticles which can increase the adsorption processes and removal efficiency. However, the adsorption rate was decreased by increasing of the Cu(II) concentration and occupation of active adsorption sites 47 . The adsorption at different concentrations is rapid in the initial stages and gradually decreases during the progress of adsorption until the equilibrium is reached 30 . The high adsorption rate at the beginning was due to the adsorption of copper ions by the exterior surface of the adsorbent. When saturation was reached at the exterior surface, the metal ions entered the pores of adsorbent and were adsorbed by the interior surface of the particles 9 . The initial faster rates of adsorption may also be attributed to the presence of large number of binding sites for adsorption and the slower adsorption rates at the end is due to the saturation of the binding sites and attainment of equilibrium 31 . Based on Van Tran et al. (2020), the adsorbent surface is saturated at higher levels of initial concentration, which is attributable to enhanced affinity of the interactions between molecules and  www.nature.com/scientificreports/ adsorption sites on adsorbent surface until reaching a saturation threshold. Previous researches showed that the initial metal concentration have important role for mass transfer between the aqueous and solid phases 37 . In order to environmental remediation, the optimum initial concentration of γ-Al 2 O 3 and TiO 2 were reported as 8 mg L −1 and 15 mg L −1 in previous researches 10 .
In water, salt is present in a wide range of concentrations depending on the source and the quality of the water 22 . The presence of salt leads to high ionic strength, which may significantly affect the performance of the adsorption process. The reason for this is that Ca 2+ and Na + ions in the aqueous phase compete effectively with positively charged Cu(II) ions for the same binding sites on the adsorbent surface 27 . Additionally, salt screens the electrostatic interaction between adsorbent and adsorbate and the great ionic strength influences on the activity coefficient of Cu(II), which should decrease the adsorbed amount with the increase in salt concentration 51 . Same results were reported by Van Tran et al. (2020), who found that the ionic strength and background electrolyte affected the adsorption competition.
The results of previous studies on Cu(II) adsorption using different nanoparticles are presented in Table 7. Comparison of the obtained results with previous studies shows that more Cu(II) concentration was adsorbed with TiO 2 and γ-Al 2 O 3 nanoparticles (the present study) than others (Table 7). Meanwhile, the results show that the equilibrium in the process of Cu(II) adsorption by TiO 2 and γ-Al 2 O 3 nanoparticles (the present study) was obtained earlier than other adsorbents.
The spontaneous nature of the adsorption processes can be determine by negative values of Gibbs free energy (ΔG°), means that no energy input from outside of the system is required 6 and the exothermic performance of the adsorbed and the adsorbent interaction. If the ΔG° values ranged from − 20 to 0 kJ mol −1 , it means the physical adsorption process 24 . The more negative values of ΔG° imply a greater driving force to the adsorption process, and confirming that the adsorption of Cu(II) onto the nanoparticles is spontaneous and thermodynamically favorable. According to the results, increase of pH caused to increase of the kinetic of adsorption reactions. The spontaneous nature was slowly found at the lowest pH than the highest. The values of ΔH° are negative, indicating that the adsorption process is exothermic in nature. The negative values of ΔS° indicate greater order of reaction during adsorption of Cu(II) on the nanoparticle surface.

Conclusion
In general, the results of the present study showed that TiO 2 and γ-Al 2 O 3 nanoparticles, especially TiO 2, had the high capacity for Cu(II) removal from aqueous solution. Speciation data showed that the physical adsorption was the main mechanism for Cu(II) removal. The removal efficiency was increased by increasing pH and initial concentration. The ionic strength had the inverse effect on Cu(II) adsorption, which decreased significantly in the presence of 0.01 M CaCl 2 background electrolyte. The spontaneous adsorption processes was occur at different pH values. Using of TiO 2 nanomaterial is an effective method for elimination of Cu(II) polluted environments.  Table 7. Copper (Cu(II)) adsorption with different adsorbents.