Introduction

4-Nitrophenol (4-NP) contributes significantly to many industries including dyes (Das and Dey 2020), leathers (Tugba Saka and Tekintas 2020), plastics (Chakraborty 2016) and pharmaceuticals (Wróbel et al. 2000). However, it is a persistent and hazardous organic pollutant with high toxicity and have a serious effect on ecosystem even at very low concentration (Han et al. 2019; Park et al. 2010). 4-NP is considered to be hazardous to human health, aquatic life and plants (Teimouri et al. 2018). Its toxicity can cause health-associated problems to humans such as liver and kidney damage, mouth irritation, blurred vision and systemic poisoning (Fatima et al. 2019; Sellaoui et al. 2019). The continuous ingestion of 4-NP into water and soil requires intensive remediation efforts. This is attributed to the structure stability provided by the nitro group presents in 4-NP, which leads to continues accumulation of 4-NP in water and soil bodies (Kaur et al. 2019). Therefore, it is considered as a major pollutant by environmental protection Agency (EPA) (Eckenfelder 2000), which specified an allowable maximum NPs concentration levels of 0.01–2.0 μg/L in wastewater streams (Kupeta et al. 2018).

Varity of conventional methods are used to treat 4-NP from wastewater and industrial effluents to meet discharge and environmental regulations, including membrane separation, solvent extraction, chemical oxidation, biological treatment, etc.… However, these technologies are generally associated with high operational cost, time consuming, and formation of other by-products (Ewis et al. 2021; Liu et al. 2020). Adsorption is a simple, feasible, economical and cost-effective method that can significantly reduce the concentration of containments, especially with the availability of a wide range of adsorbents with interesting physicochemical properties such as Bentonite clay (Foucaud et al. 2018; Mignon et al. 2020; Saleh et al. 2019; Wadi et al. 2020). Bentonite consists of two silica (SiO2) tetrahedral sheets with a central alumina oxide (Al2O3) octahedral sheet (Nakamura et al. 2020). Furthermore, bentonite is characterized by several unique physicochemical properties including large specific area, low cost, high porosity, and swelling capability (Gupt et al. 2020; Mudasir et al. 2020). These unique properties endow bentonite with excellent capability to remove water pollutants from aqueous solutions. Kyzioł-Komosińska et al. (Kyzioł-Komosińska et al. 2019), studied the adsorption of Europium (III) ions using bentonite with maximum reported removal efficiency of 99.9% and a maximum adsorption capacity suggested by sips isotherms model ranged from 2.103 to 77.56 mg/g. However, bentonite still have relatively lower adsorption capability compared to other nanomaterials. Consequently, bentonite can be modified or coupled with other nanomaterials to improve its adsorption capability (Aytas et al. 2009; Lin et al. 2018; Wang et al. 2014). Recently, several authors reported that bentonite supported metals oxides are efficient for water remediation (Babu and Antony 2019; Honarmand et al. 2020; Khatamian et al. 2019; Mishra et al. 2018; Pourshadlou et al. 2020). However, decreased metals oxides particle sizes and the particles aggregation degree are crucial to enhance its adsorption capability (Zhou et al. 2019). The application of Sonochemical during the nanocomposite synthesis can greatly enhance the distribution of metals oxides over the support layer and reduce the metal oxide particle size (Potle et al. 2020) which enhances the adsorption capability of the nanocomposite. Furthermore, the addition of metal oxides with magnetic properties enable nanocomposite separation from aqueous solution (Farooghi et al. 2018).

The aim of this work is to investigate the removal efficiency of 4-NP from wastewater using Iron oxide/Bentonite nanocomposite (Fe3O4/Bt. NC) and determine the optimum process parameters using Response Surface Methodology (RSM). RSM has been extensively used to optimize the operational variables for the maximum adsorption capacity, RSM correlates between independent and dependent (response) variables through mathematical and statistical methods (Çelebican et al. 2020). Nevertheless, the literature shows no comprehensive optimization of operational variables for 4-NP maximum removal capacity with any adsorbent using RSM technique. In this work, Box–Behnken design, which is one of the RSM experimental design techniques, was used for the first time to optimize the experimental parameters of adsorbent dosage, solution pH, solution concentration and contact time. Furthermore, the adsorption isotherms and kinetics data for the removal of 4-NP from wastewater using Iron oxide/Bentonite nanocomposite (Fe3O4/Bt. NC) were included with a possible mechanism for 4-NP adsorption onto Fe3O4/Bt. NC was proposed.

Experimental

Chemical and Materials

4-nitrophenol (C6H5NO3) (98%, purity) and ammonia solution 25% (NH4OH), ferric chloride (FeCl3.6H2O) (99%, purity), ferrous chloride (FeCl2.4H2O) (99%, purity), were purchased from Research Lab fine chem industries.

Fe 3 O 4 /Bt. NC Synthesis

Fe3O4/Bent NC was fabricated by co-precipitation method assisted using ultrasound as reported in our previous work (Ewis et al. 2020). Briefly, 2.5 g of ferrous chloride (FeCl2.4H2O) and 1.5 g of ferrous chloride (FeCl3.6H2O) were dissolved in 100 ml of distilled water under nitrogen environment (solution I). Then, 2 g of bentonite was dispersed in 15 ml of ammonia (25%) at 50 ℃ for 30 min (solution II). After that, solution I was slowly added into solution II and the mixture was stirred for 1 h. The mixture was then placed in an ultrasonic bath for 3 h. Finally, the obtained nanocomposite was separated by a magnet, washed several times with distilled water and ethanol, and dried in a vacuum oven.

Characterization

The characterization of Fe3O4/Bent NC including X-ray diffraction (XRD), Transmission Electron Microscopy (TEM), Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray (EDX), Vibrating Sample Magnetometer (VSM) and Fourier-Transform Infrared Spectroscopy (FTIR) were reported in our previous study (Ewis et al. 2020). The surface properties of the nanocomposite after adsorption was investigated by FTIR analyzer over the range 4000–500 cm−1.

Adsorption Study

The adsorption experiments were conducted in a 150 ml Erlenmeyer flask and were continuously stirred in an air bath shaker at 200 rpm and constant temperature of 30 ℃ for certain times varying from 30 to 210 min as indicated in Table 4. The solution pH was adjusted as needed using HCl (0.1 mol/L) or NaOH (0.1 mol/L). 4-NP concentration was determined using a concentration-absorbance calibration curve obtained at λ = 317 nm using a Perkin Elmer Lambda 35 UV/Vis spectrometer. The amount of 4-NP absorbed per unit weight of adsorbent (mg of 4-NP/g of adsorbent) was calculated using Eq. 1:

$${q}_{e}=\frac{{C}_{o}-{C}_{e}}{m}V$$
(1)

where C0 and Ce are the initial and equilibrium concentration of 4-NP (mg/L), V is the solution volume (L), qe is the equilibrium adsorption capacity and m is the amount of dry mass of nanoparticles added to the solution (g).

The performance of Fe3O4/Bent NC for 4-NP removal was account as removal efficiency (RE) using Eq. 2 below.

$$\mathrm{RE} (\%)= \frac{{C}_{o}-{C}_{e}}{{C}_{o}}\times 100\%$$
(2)

Statistical Optimization Analysis Using Box–Behnken Design

Batch experiments were designed using Minitab 19 software by implementing Box–Behnken design (BBD) to examine the effect of four indented variables on the response function (adsorption). The BBD experiments involved three levels and four independent variables resulting in 27 experimental runs. The independent variables were adsorbent dosage, initial 4-NP concentration, solution pH and contact time. Table 1 shows the combination of experiments given by BBD for RSM. The relationship between the response and independent variable is demonstrated by second-order non-linear quadratic polynomial (Ba-Abbad et al. 2015, 2017) as represented by Eq. 3:

$$Y={\beta }_{o}+ \sum_{i=1}^{n}{\beta }_{i}{X}_{i}+ \sum_{i=1}^{n-1}\sum_{j=i+1}^{n}{\beta }_{ij}{X}_{i}{X}_{j}+ \sum_{i=1}^{n}{{\beta }_{ii}X}_{i}^{2}+\varepsilon i<j$$
(3)

where Y is the response, n is the number of variables and ε is the random error. \({\beta }_{o}, {\beta }_{i}, {\beta }_{ii} and {\beta }_{ij}\) are the model intercept coefficient, liner effect coefficient, quadratic effect coefficient and interaction effect coefficient, respectively. \({X}_{i}{ and X}_{j}\) are the level of independent variables. The coefficients were determined by least-square regression followed by an analysis of variance (ANOVA). The data were fitted with a quadratic equation and the effect of the process variables on adsorption capacity of Fe3O4/Bent NC were observed by RSM.

Table 1 Isotherm models’ parameters
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Results and Discussion

Adsorption Isotherms

Adsorption isotherm studies are important to understand the adsorption mechanism by indicating the interaction type between the adsorbate and the adsorbent (Ewis and Hameed 2021). The adsorption isotherm models for solid–liquid systems are represented by a relationship between the amount of adsorbate adsorbed onto an adsorbent \(({q}_{e}\)) and the liquid concentration \({(C}_{e})\) at constant temperature. In this study, Freundlich, Halsey, Temkin and Dubinin-Radushkevitch (D-R) equilibrium isotherm models were employed to fit the adsorption data. Freundlich isotherm model assumes a multilayer adsorption on a heterogeneous surface. The model is described by Eq. 4 (Do 1998).

$${q}_{e}={K}_{f}{C}_{e}^\frac{1}{n}$$
(4)

The linearized form of Freundlich isotherm model is described by Eq. 5.

$$\mathrm{ln}{q}_{e}=\mathrm{ln}{K}_{f}+\frac{1}{n}\mathrm{ln}{C}_{e}$$
(5)

where \({K}_{f}\) is the adsorption capacity in (mg g−1)(L mg−1)1/n and \({C}_{e}\) is the equilibrium concentration in mg L−1, n is an indicator of the adsorption intensity.

Similar to Freundlich model, Halsey isotherm model describes multilayer adsorption on a heterogeneous surface, where the adsorption heat is distributed non-uniformly. In addition, the model evaluates a multilayer adsorption system at relatively large distance from the system. The model is expressed by Eq. 6 (Gholitabar and Tahermansouri 2017).

$${q}_{e}= {\mathrm{e}}^{\left[ln{K}_{H}-ln{C}_{e}\right]/{n}_{H}}$$
(6)

where \({K}_{H}\) and \({n}_{H}\) are Halsey isotherm constant. The linearized form of Eq. 6 is represented by Eq. 7.

$$\mathrm{ln}{q}_{e}=\left(\frac{1}{{n}_{H}}\right)\mathrm{ln}{K}_{H}-\left(\frac{1}{{n}_{H}}\right)\mathrm{ln}{C}_{e}$$
(7)

Temkin isotherm model (Samarghandi et al. 2009) evaluates the adsorption heat, it assumes that the adsorption heat of all molecules present in a layer decreases linearly due to the increase in surface coverage. Temkin isotherm model is described by Eq. 8.

$${q}_{e}=B\mathrm{ln}(A{C}_{e})$$
(8)

where B is related to the adsorbent heat in (J mol−1) and A is the equilibrium-binding constant that corresponds to the maximum binding energy. The model can be linearized and described by Eq. 9.

$${q}_{e}=B \mathrm{ln}A+B\mathrm{ln}{C}_{e}$$
(9)

D-R isotherm model describes the mechanism of adsorption with Gaussian energy distribution onto heterogeneous surfaces. The model is based on a multilayer adsorption assumption in which it involves Van Der Waal’s forces (Günay et al. 2007). The model is described by Eq. 10.

$${q}_{e}= {Q}_{DR}\mathrm{exp}\left(-{K}_{DR}{\left[RTln\left(1+\frac{1}{{C}_{e}}\right)\right]}^{2}\right)$$
(10)

where \({q}_{e}\) is the amount adsorbed in mmol g−1, \({Q}_{DR}\) is the maximum adsorption capacity in mmol g−1 and \({K}_{DR}\) is the Dubinin–Radushkevich isotherm constant in mol2 K−1 J−2. The model can be linearized and expressed by Eq. 11.

$$\mathrm{ln}{q}_{e}=\mathrm{ln}{Q}_{\mathrm{DR}}-{K}_{\mathrm{DR}}{\left[\mathrm{RTln}\left(1+\frac{1}{{C}_{e}}\right)\right]}^{2}$$
(11)

Batch adsorption experiments were carried out for 90 min at pH 5 and adsorbent dosage of 0.1 g at 30 ℃. The linearized plots of the mentioned isotherm models are shown in Fig. 1 with the characteristics parameters of each model represented in Table1. Based on the obtained data, Freundlich model best described the adsorption data with regression coefficient (R2) equals 0.9972. From mathematical point of view, Freundlich model assumes that the surface coverage goes to infinity; therefore, the saturation of Fe3O4/Bent NC with 4-NP cannot be evaluated. Nevertheless, the model can predicts the favorability of the adsorption process by the term n, which is less than 1 indicating that the adsorption process is unfavorable rendering it uneconomical as higher partial pressure is needed to achieve economic adsorption (McCabe and Smith 1967). Halsey isotherm model of adsorption data is fitted as second model with R2 equals 0.9954, which confirms a multilayer adsorption on a heterogeneous surface. In addition, the model confirms that the adsorption process in unfavorable since nth term 0.505, which sustains the conclusion obtained from Freundlich model. D-R model fitted the adsorption data with R2 value equals 0.9876. The model shows that the maximum adsorption capacity equals 0.6386 mmol g−1, which corresponds to 88.77 mg g−1. The obtained model parameters were used to determine mean free energy (\(E=1/\sqrt{2{K}_{DR}}\)), found to be equals to 2236 J mol−1. The value of E which is less than 8000 J mol−1 indicates a physical adsorption process (Caetano et al. 2009). Finally, Temkin isotherm model fitted the adsorption data with R2 equals 0.9307. Based on the results obtained, adsorption of 4-NP onto Fe3O4/Bent NC was best described by Freundlich model suggesting a non-ideal multiplayer adsorption process on a heterogeneous surface.

Fig. 1
figure 1

Isotherm models a: Freundlich model, b: Halsey model, c: D-R model, d: Temkin model

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To verify the synthesis of Fe3O4/Bent NC, the adsorption of 4-NP onto raw bentonite was estimated under the same experiment conditions. The experiment was carried out using 0.1 g of adsorbent, pH 5, and 4NP concentration of 100 ppm for 90 min at 30 ℃. The results showed that raw bentonite attains around 29% removal, whereas Fe3O4/Bent NC attain around 53.12%.

Adsorption Kinetics

The adsorption kinetics indicate the rate at which the adsorbent uptake the adsorbate. Pseudo first order(PFO) (Yuh-Shan 2004), pseudo-second order (PSO) (Ho and McKay 1998), and intra-particle diffusion (IPD) (Weber & Morris, 1963) models were used to interpret the adsorption kinetics. PFO shows proportional relationship between adsorption rate and the active sites number, while PSO is based on the adsorption equilibrium capacity. The linear forms of PFO and PSO models are described by Eqs. 12 and 13, respectively.

$$Ln\left({q}_{e}-{q}_{t}\right)=\mathrm{ln}\left({q}_{e}\right)-{k}_{1}t$$
(12)
$$\frac{t}{{q}_{t}}=\frac{1}{{{k}_{2}}^{2}{q}_{e}}+\frac{t}{{q}_{e}},$$
(13)

where\({k}_{1}\), \({k}_{2}\) and \(t\) are the first-order rate constant (min−1), second-order rate constant (g/mg min) and time (min), respectively.

IPD model indicates the rate-controlling step involved in the adsorption process. The model is represented by Eq. 14.

$${q}_{t}= {K}_{i}\sqrt{t}+C$$
(14)

where \({K}_{i}\) is the rate constant in (mg/g min0.5).

Data of batch experiments carried out at 100 ppm solution concentration, pH 5 and adsorbent dosage 0.1 g at a temperature of 30 ℃ were fitted the kinetic models as shown in Fig. 2. The associated numerical values of the kinetic parameters of each model are presented in Table 2. We can easily observe that the adsorption data did not fit well PFO model with obtained poor R2 value equals to 0.7322 (Fig. 2a). In contract, R2 found to be equal to unity (R2 = 1.000) for the PSO kinetic model fitting (Fig. 2b) indicating a good fitting. To understand the adsorption mechanism, it is important to have insights into the rate-controlling step provided by IPD kinetic model shown in Fig. 2c where two complex steps are involved in the adsorption process. The first stage illustrates an instantaneous adsorption characterized by a sharp increase in 4-NP uptake with rate constant ki1. The second stage depicts a gradual increase in 4-NP uptake controlled by intra-particle diffusion with rate constant ki2. The last stage indicates that equilibrium was reached, but an internal diffusion in the pores occurs with rate constant ki3. The numerical values of rate constant shows the rate-limiting step is interior pore-diffusion since ki1 is greater than ki2 and ki3 (Table 2). The constant C in Eq. 14 represents the influence of the boundary layer. The higher C value, the greater is the influence of boundary layer on the adoption process (Hameed 2007). From Fig. 2 c, it is observed that the influence of the boundary layer was significant in the second and the third stages. Furthermore, it can be seen that the C does not attain zero, which confirm the rate-limiting step not including of the intra-particle diffusion.

Fig. 2
figure 2

The kinetic models of 4-NP adsorbed onto Fe3O4/Bent NC using a PFO, b PSO, c IPD models

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Table 2 Numerical values of the kinetics models parameters
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Box–Behnken Design (BBC) Statistical Analysis

RSM consists of statistical and mathematical techniques, which help to develop models that predict the influence of variables on a response function and optimize the response. BBD was used to optimize the removal of 4-NP using Fe3O4/Bent NC by examining the effect of adsorbent dosage, solution initial concentration, solution pH and contact time at fixed temperature of 30 ℃. BBD suggested a random order of 27 experiments, which were carried out in duplicates. The level and the range of independent variables are shown in Table 3. The experiments results were processed by the software (Minitab 19) to obtain the theoretical removal. Table 4 shows the 27 experiments suggested by BBD using Minitab 19 with the corresponding actual and predicted removal efficiency. The theoretical removal generated by the software using least-square method is represented by a quadratic polynomial (Eq. 15), where \({\mathrm{x}}_{1}\) is the dosage, \({\mathrm{x}}_{2}\) initial concentration, \({\mathrm{x}}_{3}\) solution pH, \({\mathrm{x}}_{4}\) contact time. A positive sign indicates a synergistic effect of the factors, while the negative sign indicates an antagonistic effect.

Table 3 Independent variables range and level
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Table 4 RSM design matrix and response function values
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$$\mathrm{Removal \%}=54.40+49.3 {\mathrm{x}}_{1}+0.695{\mathrm{x}}_{2}-1.01{\mathrm{x}}_{3}+0.0667{\mathrm{x}}_{4}-54.2{\mathrm{x}}_{1}{\mathrm{x}}_{1}-0.003032{\mathrm{x}}_{2}{\mathrm{x}}_{2}+0.1780{\mathrm{x}}_{3}{\mathrm{x}}_{3}-0.000391{\mathrm{x}}_{4}{\mathrm{x}}_{4}-0.230{\mathrm{x}}_{1}{\mathrm{x}}_{2}-0.47{\mathrm{x}}_{1}{\mathrm{x}}_{3}+0.0719{\mathrm{x}}_{1}{\mathrm{x}}_{4}-0.01216{\mathrm{x}}_{2}{\mathrm{x}}_{3}+0.000195{\mathrm{x}}_{2}{\mathrm{x}}_{4}+0.00011{\mathrm{x}}_{3}{\mathrm{x}}_{4}$$
(15)

The regression model was evaluated and the ANOVA results are represented in Table 5. The F values and p values determine the significance of each coefficient. The regression model explains well the variation in the response, as the F values are higher. Furthermore, the associated p value determines the statistical significances by estimating if Fstatistical is large enough. A p value < 0.05 indicates that the regression model is statistically significant at 95% confidence level (Mohammad et al. 2019). In this study, the regression model was found to be statistically significant as the F value corresponded to 18.19 with a p value equals to zero (0.000). The determination coefficient (R2) attained a value of 95.50%, which suggests that the experimental values are in good agreement with the values obtained from the regression model. In addition, the adjusted R2 was 90.25%, which confirms the statistical significance of the regression model and indicate that the model can be used for further analysis.

Table 5 Variance analysis
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The regression model and RSM were used to evaluate the effect of four independent variables on 4-NP adsorption. Figure 3A, B, C, D, E and F shows 3D surface response plots of two factors while keeping other factors constants. Generally, the interaction between all selected variables was insignificant as the p values were higher than 0.05 (Table 5). The 3D surface plot for the optimization of adsorbent dosage with initial solution concentration is shown in Fig. 3A. The figure clearly shows that 4-NP removal increases as the adsorbent dosage and initial solution concentration increased. This could be attributed to the combination effect of active sites availability as the adsorbent dosage rises and an increase in the concentration gradient between bulk solution and the adsorbent as the initial solution concentration increases. The contour plot shown in Fig. 3G indicate that the maximum removal could be obtained is approximately 95%. Figure 3C shows 3D surface plot for the study effect between the dosage of adsorbent and contact time on 4-NP removal percentage. Generally, the removal of 4-NP increases as the adsorbent dosage and contact time increases. This may be due to the availability of active sites available with adsorption along with the time available that allow more interaction between the adsorbent and 4-NP molecules. As time increases, the vacant sites become occupied and equilibrium is reached. The contour plot shown in Fig. 3I shows that the maximum removal percentage is around 92%. Similar trend is observed for the combined effect of contact time and initial solution concentration on 4-NP removal (Fig. 3F). The contour plot shown in Fig. 3L shows that the maximum removal percentage is around 95%.

Fig. 3
figure 3

A–F 3D surface plot G–L contour plots for 4-NP adsorption

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The removal of 4-NP using Fe3O4/Bent NC is highly pH dependent as can observed from Fig. 3 B, D and E. The figures indicate that the adsorption is maximum under basic conditions. The contour plot shown in figure H and K shows that 96% removal can be obtained at higher dosage and contact time under basic conditions, respectively. In addition, at higher initial solution concentration (e.g. 90 ppm), 4-NP can be completely adsorbed onto Fe3O4/Bent NC at a pH of 11 as shown in Fig. 3J.

Varying the pH have a significant impact on the adsorption process as it affects Fe3O4/Bent NC surface charge and 4-NP degree of ionization. In order, 4-NP have a pka value of 7.15, which means that at a pH lower than 7.15, 4-NP tend to stay at on its molecular form. However, at a pH higher than 7.15, 4-NP dissociates onto 4-NP anions form (Dhorabe et al. 2017). The results obtained from RSM suggests that the adsorption is maximum at pH 11. This could be attributed to the availability of 4-NP anions and its reduction products with Fe3O4/Bent NC. The mechanism of adsorption is further discussed in "Adsorption Mechanism"

The optimization results by RSM are shown in Table 6 in which 100% is the optimum removal that can be obtained at the indicated values of independent variables. The removal of 4-NP at the optimization solution was verified. The results suggests that around 99.5% of 4-NP was adsorbed onto Fe3O4/Bent NC, which confirms the validity of the regression model. Pareto analysis reveals the relative importance of different independent variables by computing the percentage effect of each independent variable on the response. The Pareto analysis (Fig. 4) suggest that initial 4-NP concentration and pH are the most important factors in the adsorption studies.

Table 6 Optimization solution
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Fig. 4
figure 4

Pareto chart for standardized effects

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Adsorption Mechanism

FTIR spectra were performed on Fe3O4/Bent NC before and after adsorption to reveal the surface functional group as shown in Fig. 5. The FTIR spectra represented in Fig. 5a shows the presence of –OH and –FeOO– at absorption peak 3604 cm−1 and 1692 cm−1, respectively. In addition, the peak at 1450 cm−1 can be ascribed to the presence of Si–OH and other peak at 1038 cm−1 for Si–O–Si group is investigated. Furthermore, the figure shows the presence of Si–O, Si–O–Al and Fe–O at the absorption peak 800 cm−1, 609 cm−1 and 598 cm−1, respectively. These results confirm the presence and the formation of Fe3O4/Bent NC. Figure 5b obtained after adsorption, reveals the formation of new peak and increase in few peaks sharpness. As can be seen, the peak sharpness intensity has increased for Si–O, –OH and Si–O–Si groups. The new peaks formed at 755 cm−1 and 846 cm−1 attributed for C–H group while the peaks at 694 cm−1 and 1596 cm−1 could be attributed to the presence of C=C. The absorption peak at 1120 cm−1, 1292 cm−1 and 1341 cm−1 indicate the presence of C–O, C–N and Si=O groups. Finally, the new peak formed at 1501 cm−1 could be due to N–O group.

Fig. 5
figure 5

FTIR spectra a before adsorption b After adsorption

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The adsorption mechanism in basic conditions (e.g. pH = 11) is presented in Fig. 6. The adsorption process is expected to occur mainly through van der waals, hydrogen bond, π–π interaction, anion–π interaction, cation–π interaction. As mentioned, 4-NP converts into 4-NP anions when pH > pKa. Consequently, 4-NP anions interact with Fe3O4/Bent NC surface functional groups to form chemical bonds. The adsorption kinetics results suggested that the adsorption follows PSO kinetic model, which confirm a chemisorption process. FTIR spectra presented in Fig. 5 confirm the formation of new bond on Fe3O4/Bent NC surface after adsorption. Furthermore, the presence of H+ ions on Fe3O4/Bent NC surface, which came originally from aqueous solution cause a redox reaction to produce 4-aminophenol as presented in Fig. 6. This explains the presence of C–N and N–O groups in FTIR spectra (Fig. 5b). The aromatic ring exists in 4-NP contains π electrons which interacts with π electrons exists on Fe3O4/Bent NC surface resulting in a strong π–π interaction. The FTIR spectra confirms such interaction by the existence of C=C bond. Furthermore, the bond of O–H indicates a dipole–dipole hydrogen bonding interaction, which is also a type of electrostatic interaction. n–π interaction could be possible interaction, which occur when lone pair of electrons on oxygen delocalize into π orbital of an aromatic ring. In fact, anion–π interaction might be involved in the adsorption process. (Alkorta et al. 2002) studied the interaction between several anions including [H]−, [F]−,[Br]−,[CN]− and [CNO]− and aromatic ring through Density Functional Theory (DFT) and MPs calculations. The study revealed that these anions interaction favorably with the π cloud of the aromatic ring. In this study, the FTIR spectra revealed the presence of C–N bond, which indicates that anion–π interaction is present. Furthermore, cation–π interaction is also involved in the adsorption process due to the availability of cations including Na+ and Mg+2 (Schottel et al. 2008) mentioned that cations also interact with π cloud of the aromatic ring. However, the relative strength of cation–π to anion–π interaction strongly depends on the quadrupole moment and the polarizability of the aromatic system. Finally, the EDX results on our previous work revealed the presence on positive ion such as Na+ and Mg+2 (Ewis et al. 2020). Therefore, there could be an electrostatic interaction between negative ions (4-NP, 4-NP anions and 4-aminophenol) and positive ions. In addition, the FTIR spectra prove the presence of electrostatic interaction through Fe–O and Si–O. The isotherm results suggested that adsorption data followed Freundlich isotherm model, which support the presence of physical interaction in the adsorption process.

Fig. 6
figure 6

The mechanism of 4-NP adsorption onto Fe3O4/Bent NC

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Conclusion

In this study, the adsorption of 4-NP onto Fe3O4/Bent NC was optimized by response surface methodology through Box–Behnken design framework at 30 ℃. The optimum conditions of solution pH 11, initial concentration 85 mg/L, contact time of 137.2 min and adsorbent amount of 0.318 g were obtained for 100% 4-NP removal. The maximum removal achieved experimentally was 99.5% under the optimum conditions, which proves the high capability of the studied adsorbent. The regression model results in R2 value of 95.5%, which proves the model validity. According to the statistical analysis results, 4-NP initial concentration has the most effect on 4-NP adsorption. Furthermore, the adsorption is higher in basic condition due to the availability of 4-NP anions and 4-aminophenol that are resulted from 4-NP reduction reaction on Fe3O4/Bent NC surface. Upon elucidating the mechanism, it is suggested that the adsorption process involves physical and chemical interaction. The process of 4-NP adsorption onto Fe3O4/Bent NC was best described by Freundlich isotherm model and PSO kinetic model.