*Article* **Factorial Design Statistical Analysis and Optimization of the Adsorptive Removal of COD from Olive Mill Wastewater Using Sugarcane Bagasse as a Low-Cost Adsorbent**

**Fatima Elayadi 1,2, Mounia Achak 2,3,\*, Wafaa Boumya 4, Sabah Elamraoui 2, Noureddine Barka <sup>4</sup> , Edvina Lamy <sup>5</sup> , Nadia Beniich <sup>6</sup> and Chakib El Adlouni <sup>1</sup>**


**Abstract:** This work highlights the elimination of chemical oxygen demand (COD) from olive mill wastewater using sugarcane bagasse. A 25−<sup>1</sup> fractional factorial design of experiments was used to obtain the optimum conditions for each parameter that influence the adsorption process. The influence of the concentration of sugarcane bagasse, solution pH, reaction time, temperature, and agitation speed on the percent of COD removal were considered. The design experiment describes a highly significant second-order quadratic model that provided a high removal rate of 55.07% by employing optimized factors, i.e., a temperature of 60 ◦C, an adsorbent dose of 10 g/L, a pH of 12, a contact time of 1 h, and a stirring speed of 80 rpm. The experimental data acquired at optimal conditions were confirmed using several isotherms and kinetic models to assess the solute interaction behavior and kind of adsorption. The results indicated that the experimental data were properly fitted with the pseudo-first-order kinetic model, whereas the Langmuir model was the best model for explaining the adsorption equilibrium.

**Keywords:** adsorption; sugarcane bagasse; olive mills wastewater; factorial design

#### **1. Introduction**

Olive mill wastewater (OMW) is an important environmental problem due to the strong color, low pH, and high concentrations of chemical oxygen demand (COD), biochemical oxygen demand (BOD), and phenolic compounds [1,2]. The discharge of this effluent into soil or rivers without treatment leads to damages to the environment that are attributed to their potential risk to flora or depletion of clean water reservoirs [3]. Hence, the treatment of OMW is a great challenge in a problematic environment. There are numerous approaches that can be used to remove organic pollutants and reduce COD levels from OMW such as coagulation–flocculation, aerobic and anaerobic biodegradation, solar distillation, adsorption, and infiltration percolation [4–10]. Among these techniques, adsorption is a well-established technique that benefits from advantages such as a simple design, low-cost, eco-friendly, high ability, non-generation of secondary pollutants, and reusability [11]. Activated carbon is one of the most efficient adsorbents used to treat many

**Citation:** Elayadi, F.; Achak, M.; Boumya, W.; Elamraoui, S.; Barka, N.; Lamy, E.; Beniich, N.; El Adlouni, C. Factorial Design Statistical Analysis and Optimization of the Adsorptive Removal of COD from Olive Mill Wastewater Using Sugarcane Bagasse as a Low-Cost Adsorbent. *Water* **2023**, *15*, 1630. https://doi.org/10.3390/ w15081630

Academic Editor: Hai Nguyen Tran

Received: 20 March 2023 Revised: 18 April 2023 Accepted: 19 April 2023 Published: 21 April 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

kinds of effluents. Nevertheless, it is relatively expensive, and its regeneration is difficult, which has necessitated the search for alternative adsorbents [12].

Therefore, several industrial and agricultural wastes exhibit considerable potential for the removal of pollutants. Several agricultural by-products are investigated for COD removal from wastewater, including bamboo [13], date pit [14], raw bagasse [15], areca catechu fronds [16], and date palm waste [17]. Among the efficient agro-industrial residues is sugarcane bagasse, which has a high cellulose (45%), hemicellulose (28%), and lignin (18%) content [18]. These materials contain reactive functional groups such as carboxylic and hydroxyl groups with the potential to adsorb organic loads [19]. Several studies have been conducted to investigate the sugarcane bagasse as an adsorbent for different pollutants, including Congo red, Pb(II), phenol from water, and phenol from OMW [20–24].

Although several studies have been conducted using sugarcane bagasse to remove several organic dye contaminants, no investigation has been carried out on the mathematical modeling and statistical optimization of the COD removal from OMW using sugarcane bagasse. Therefore, the present work aimed at the statistical optimization and modeling of key experimental parameters using fractional factorial design and response surface methodology (RSM) to attain optimum COD removal. Moreover, it recognizes the effects of five parameters, such as adsorbent dosage, pH, agitation time, stirring speed, and temperature as well as their interactions on the adsorption process of COD by sugarcane bagasse. Additionally, the adsorption kinetics, equilibrium, and thermodynamics were also studied.

#### **2. Materials and Methods**

#### *2.1. OMW Sample*

An OMW solution was obtained from an olive oil mill in Marrakech City, Morocco, during the campaign 2018/2019. This factory operates with the modern two- and threephase olive oil extraction technology. The OMW used is characterized by a COD of 347.8 g(O2)/L, a conductivity of 15.3 ms/cm, a phenolic content of 15.29 g/L and a pH of 4.27. The supplied effluent was immediately kept refrigerated at −4 ◦C to prevent any alteration in its physicochemical characteristics.

#### *2.2. Sugarcane Bagasse Material*

Sugarcane bagasse was collected from a local plant in the region of El Jadida, Morocco. The collected adsorbent was oven-dried for 24 h at 70 ◦C before being ground with a domestic electrical miller and sieved through a 50 μm screen. The obtained powder was repeatedly washed with distilled water to remove all impurities, and it was then oven dried at 100 ◦C for 48 h (Figure 1). The dried powder was used in experiments without any further treatment.

**Figure 1.** Sugarcane bagasse material steps.

The sugarcane bagasse functional groups were characterized by FTIR using Perkin-Elmer 1720-x spectrometer. The sample was mixed with KBr at a mass ratio of 1:100 and finely powdered and pressed to pellets. The infrared spectra were recorded in the range of 4000–500 cm−<sup>1</sup> with 2 cm−<sup>1</sup> resolution. Therefore, X-ray fluorescence analysis was employed to investigate the chemical composition of the adsorbent using the "Axion" spectrometer.

#### *2.3. Batch Adsorption Experiments*

All reagents used in the preparation and the adsorption studies were of analytical grade. The elimination of COD from OMW using sugarcane bagasse was studied in batch mode using shut-top pyrex bottles comprising 100 mL of OMW and an appropriate mass of adsorbent, which were stirred during the desired time in an incubator (Stuart SI50). The remaining COD content was determined from the UV-Vis absorbance characteristic using a Jenway 6320D UV/Vis spectrophotometer. The wavelength of maximum absorption (λmax) was 620 nm for this measurement. The quantity adsorbed and the removal efficiency of COD were calculated by measuring the solution's concentration before and after adsorption using the following equations:

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

$$\% \text{Rem} = \frac{(\text{C}\_o - \text{C}\_e)}{\text{m}} \times 100 \tag{2}$$

where qe is the amount of COD removed by the adsorbent (mg/g); Co and Ce are, respectively, the concentrations of COD before and after the batch adsorption study (mg/L); m is the mass of sugarcane bagasse (g); and V is the volume of the solution (L).

The nonlinear optimization method was used to fit equilibrium and kinetics data to the corresponding models. For this study, non-linear regression was applied using 8.0 software for fitting the curve. The best fit of the chosen non-linear models was determined by the use of four well-known error functions, namely the coefficient of determination (R2), adjusted determination coefficient (adj-R2), reduced chi-square (red-χ2), and Bayesian information criterion (BIC). A model with a higher adj-R2 value, lower red-χ<sup>2</sup> and BIC value indicates a better fitting than the others [25].

The equations of all error functions used are expressed as follows:

$$\mathbf{R}^2 = 1 - \frac{\sum \left( \mathbf{q}\_{\mathrm{i,exp}} - \mathbf{q}\_{\mathrm{i,model}} \right)^2}{\sum \left( \mathbf{q}\_{\mathrm{i,exp}} - \mathbf{q}\_{\mathrm{i,exp}-\mathrm{mean}} \right)^2} \tag{3}$$

$$\text{adj} - \text{R}^2 = 1 - (1 - \text{R}^2) \times \left(\frac{\text{N} - 1}{\text{DOF}}\right) \tag{4}$$

$$\text{red} - \chi^2 = \frac{\sum (\mathbf{q}\_{\text{i,exp}} - \mathbf{q}\_{\text{i,model}})^2}{\text{DOF}} \tag{5}$$

$$\text{BIC} = \text{N} \times \ln\left(\frac{\sum (\mathbf{q}\_{i, \text{exp}} - \mathbf{q}\_{i, \text{model}})^2}{\text{N}}\right) + \text{Pln} (\text{N}) \tag{6}$$

where qi,exp is the experimental adsorbed capacity value; qi,model is the modeled value; qi,exp–mean is an average of qi,exp values used for modelling; N is the number of experimental points; P is the number of model parameters; and DOF is the degrees of freedom.

#### *2.4. Experimental Design*

Batch experiments based on a 25−<sup>1</sup> fractional factorial design were conducted randomly to study the influence of the experimental variables on the percentage of COD removal (% Rem). The studied factors which are focused on this research; sugarcane

bagasse dose (X1), solution pH (X2), contact time (X3), stirring speed (X4) and temperature (X5). Table 1 shows the five experimental variables and their chosen levels. After performing batch experiments for the optimization process, the regression analysis was conducted to attain the study's statistical parameters with 95% confidence intervals using the Minitab 18 statistical software. The variance of the regression equation (mathematical relation between dependent and independent variables) is the most important analysis by the ANOVA method to find the desired function of COD adsorption. RSM is used as a sequential process to show the relation between the studied independent factors and the response to determine the set of optimal experimental parameters.


**Table 1.** Process factors and their levels.

#### **3. Results and Discussion**

*3.1. Analysis of Factorial Design*

25−<sup>1</sup> Fractional factorial design was adopted using Minitab 18 to optimize the influence of the investigated parameters; sugarcane bagasse dose (X1), solution pH (X2), contact time (X3), stirring speed (X4) and temperature (X5) on the elimination of COD from OMW. This design yields in 16 experiments with all possible combinations of X1, X2, X3, X4 and X5. COD removal efficiency (Y) was measured for each of these experiments as shown in Table 2. The response obtained was correlated using the second-order polynomial model, expressed by Equation (7):

$$\begin{aligned} \mathbf{Y} &= \mathbf{b}\_0 + \mathbf{b}\_1 \mathbf{X}\_1 + \mathbf{b}\_2 \mathbf{X}\_2 + \mathbf{b}\_3 \mathbf{X}\_3 + \mathbf{b}\_4 \mathbf{X}\_4 + \mathbf{b}\_5 \mathbf{X}\_5 + \mathbf{b}\_{12} \mathbf{X}\_1 \mathbf{X}\_2 + \mathbf{b}\_{13} \mathbf{X}\_1 \mathbf{X}\_3 + \mathbf{b}\_{14} \mathbf{X}\_1 \mathbf{X}\_4 + \mathbf{b}\_{15} \mathbf{X}\_1 \mathbf{X}\_5 + \mathbf{b}\_{23} \mathbf{X}\_2 \mathbf{X} + \mathbf{b}\_{24} \mathbf{X}\_2 \mathbf{X}\_4 \\ &+ \mathbf{b}\_{34} \mathbf{X}\_3 \mathbf{X}\_4 + \mathbf{b}\_{25} \mathbf{X}\_2 \mathbf{X}\_5 + \mathbf{b}\_{35} \mathbf{X}\_3 \mathbf{X}\_5 + \mathbf{b}\_{45} \mathbf{X}\_4 \mathbf{X}\_5 \end{aligned} \tag{7}$$

where Y is the COD removal efficiency response, b0 is a constant, bi correspond to linear coefficient of Xi, and bij is the interaction coefficient.

**Table 2.** Matrix design with experimental and predicted COD removal values.


The statistical calculations and regression analysis were conducted to fit the response function with the experimental data. The regression coefficient values obtained are given in the final regression equation, after putting the values of all coefficients, as follows (Equation (8)):

Y (%) = 37.85 − 2.183X1 + 0.39X2 − 0.562X3 + 0.05958X4 + 0.2182X5 − 0.01712X1X3 − 0.0529X1X3 + 0.102X1X4 − 0.0528X1X5 − 0.05223X2X3 − 0.01976X2X4 − 0.01153X2X5 − 0.003092X3X4 − 0.00436X3X5 − 0.000623X4X5 (8)

> This equation expresses the COD removal efficiency as a function of investigated experimental factors and enables fixing experimental conditions for each targeted COD removal efficiency. From Table 2, the values predicted obtained by the relation (8) are compared with those of experimental results, which show a good agreement between the two sets of values. In Equation (8), the positive signs of the coefficients for X2 and X4 factors and the X1X4 interaction imply their positive effect on the response, while the negative signs of the coefficients for X1, X3, and X5 factors as well as the X1X2, X1X3, X1X5, X2X3, X2X5, X3X4, X3X5, and X4X5 interactions represent the negative effect on the response. Based on the equation, the most influential factor for response was the sugarcane bagasse dose with a coefficient value of 2.183. The sign (-) means that each one-point decrease will have an effect of 2.183 on the COD removal efficiency value.

> To check the quality of the model fitting, an ANOVA analysis was performed using the F-value and *p*-value (Table 3). Significant effects of the model terms were identified based on the *p*-values less than 0.05. Since the F-value for the 95% confidence interval, one degree of freedom, and 16 factorial runs, was equal to 4.54, all terms of the model with a F-value greater than 4.54 are considered statistically significant. The COD removal model was determined to be greatly significant from Fisher's test (F-value of 22.24) and a smaller *p*-value (<0.0001). Besides that, the significance of the model terms can be determined through the Pareto chart (Figure 2). The Student's *t*-test was performed to evaluate the significance of the regression coefficients. For the 95% confidence interval and one degree of freedom, it was observed that the t-value was equal to 2.12. Student's t-test values for model terms are displayed in horizontal columns. All term effects are significant if their absolute values exceed the vertical line (2.12). According to the obtained F value, *p*-value and Pareto chart, it seems that the main effects X1, X2, X3, X4 and X5 factors as well as the X2X3, X1X4, X3X5, X1X2, X3X4, X4X5 and X1X3 interactions are statistically significant. In this way, the COD adsorption by sugarcane bagasse could be expressed using the following equation (Equation (9)):

$$\begin{array}{c} \text{Y (\%)} = 21.5 - 0.2439 \,\text{X}\_1 + 1.494 \,\text{X}\_2 + 1.387 \,\text{X}\_3 - 0.0451 \,\text{X}\_4 + 0.4404 \,\text{X}\_5 - 0.01712 \,\text{X}\_1 \text{X}\_2\\ - 0.00497 \,\text{X}\_1 \text{X}\_3 + 0.001892 \,\text{X}\_1 \text{X}\_4 - 0.09223 \,\text{X}\_2 \text{X}\_3 - 0.001131 \,\text{X}\_3 \text{X}\_4 - 0.01268 \,\text{X}\_3 \text{X}\_5 - 0.000743 \,\text{X}\_4 \text{X}\_5 \end{array} \tag{9}$$

To graphically verify the validity of the regression model obtained for COD removal, four validation indicator plots were created by plotting the differences between the predicted (model) and the observed (experimental) values (Figure 3).

The normal probability plot (Figure 3a) reveals that all points are reasonably near a straight line, suggesting that the predicted values of COD removal and the actual experimental data were in agreement, evidencing the normal distribution of the data and the validity of the regression model. The graphic plot of the residuals (Figure 3b) displays the predicted and observed values. The residual (vertical axis) is the difference between the experimental and the fitted values [26]. As shown in Figure 3b, the experimental points are reasonably aligned around zero, suggesting normal distribution. The histogram (Figure 3c) shows a random distribution of values with no noticeable shape or trend across all 16 runs conducted. The residuals versus the observation orders (Figure 3d) show that the residuals seem to be randomly scattered around zero.


**Table 3.** Analysis of ANOVA for COD removal. The underline shows the *p*-values < 0.05.

**Figure 2.** Pareto chart for standardized effects.

**Figure 3.** Validation indicator plots of the model for the experiments (**a**) Normal probability plot, (**b**) Versus fits (**c**) Histogram and (**d**) Versus order.

#### *3.2. Response Surface Analysis*

The significant interaction effect between each two input parameters on the % removal was presented by the 3D response surface and the contour of the plots (Figure 4A–G). The interaction between the solution pH (X2) and contact time (X3) is shown in Figure 4A. The graph demonstrated that better COD removal is achieved at acidic pH and long contact time. Further, the % removal decreased from 39 to 30% with increasing pH. This can be explained by the impact of pH on the ionic configuration of the functional groups presented in the sorbent. Previous pH drift tests indicated that the pHpzc (point of zero charge) of sugarcane bagasse is equal to 5.0, which expresses that the surface of the bagasse is positively charged at a pH below 5 and negatively charged at a pH above 5 [21,27]. As a consequence, the electrostatic interaction between the adsorbent surface and the organic matter could be enhanced, which resulted in high adsorption of COD [28]. The interaction effect between the adsorbent dose (X1) and agitation speed (X4) shown in Figure 4B reveals that there will be a good COD removal equal to 44% when the adsorbent dose is 10 g/L and also at the stirring speed of 80 rpm. Nevertheless, an adsorbent dose greater than 10 g/L and an agitation speed above 80 rpm result in a substantial decrease in % removal. This phenomenon is due to the aggregation and glomeration of the sorbent particles and the reduction in the total surface area of the adsorbent [29].

**Figure 4.** *Cont*.

**Figure 4.** (**A**–**G**): Response surface and contour plots for the % Rem.

Figure 4C describes the interaction between the temperature (X5) and reaction time (X3) on the % removal. The % removal increases with an increase in temperature, whereas it decreases with increasing contact time. There are more active surface sites when the temperature increases because the adsorbent swells more as well. This result suggests that the COD adsorption is an endothermic process [30]. As shown in Figure 4D, the interaction between the pH (X2) and adsorbent dose (X1) has a negative effect on % removal. An increase in either of these two parameters reduces the COD removal efficiency. The possible reason for such observations is that the changes in pH could lead to changes in the properties of the surface of adsorbent [31], and the agglomeration of the adsorbate particles occurs with an increase in the adsorbent dose. In Figure 4E, the % removal decreased with increasing contact time (X3) and stirring speed (X4). The % removal was very rapid in the beginning stages of contact time, reaching about 38%. This result was related to the availability of more active sites on the adsorbent and the fact that the gradient of concentration between the adsorbate molecules in the solution and the adsorbate molecules on the adsorbent is high, which improves COD diffusion to the adsorbent surface [32,33]. After 60 min, the removal decreases over time due to adsorption site saturation, and the adsorbate molecules may bind poorly to the active receptors on the adsorbent [34,35].

Furthermore, the obtained results represented in Figure 4F show that the optimum of % removal was found at a temperature of 60 ◦C and agitation speed of 80 rpm, giving 39% of the recovery. This tendency may be due to the breakdown of the expanding chain and flocs. However, the higher stirring speed can encourage the process of agglomeration [36]. Additionally, the % removal was decreased by increasing the adsorbent dosage (X1) and contact time (X3) (Figure 4G). The % removal is initially higher and more rapid, reaching up to more than 38% within the first 60 min and 10 g/L, which could be attributed to the greater availability of adsorption sites to bind COD and a greater adsorbent active sites/COD ratio [37]. Subsequently, it decreased slightly with the prolonging of the contact time and the increase in adsorbent dose, which could be caused by an aggregation of the adsorbent and the reduction of the surface area available to COD [20].

#### *3.3. Optimization Process*

The response optimization study used to determine the combination of optimal values of the factors for maximum COD removal is shown in Figure 5. The validation of the optimal conditions is regarded as the final step in the modeling approach to examine the precision and robustness of the investigated model. The optimization requests to determine the desired pH, contact time, temperature, stirring speed, and adsorbent concentration to attain significant desirability. The model optimization shows that the efficiency of COD removal increases with an increasing pH, contact time and temperature, while decreasing with an increasing adsorbent dose and stirring speed. The optimum conditions indicate that the experimental modeling yields a desirability close to 1 with 55.07% COD removal efficiency, in the subsequent conditions: 10 g/L doses of sugarcane bagasse, 1 h of contact time, 80 rpm of stirring speed, 60 ◦C of temperature, and pH 12 of the OMW solution.

#### *3.4. Characterization of Sugarcane Bagasse*

Sugarcane bagasse characterization is an important analysis for understanding the behavior or the mechanism of COD removal on its surface. X-ray fluorescence elemental analysis was used to identify and quantify the amount of the elements contained in sugarcane bagasse in order to ultimately determine its elemental composition. The mineral composition of sugarcane bagasse is presented in Table 4. The results demonstrate that sugarcane bagasse contains a high amount of silicon dioxide (SiO2), as high as 62.23%, and low amounts of alkaline oxide, Al2O3 and P2O5.

**Figure 5.** Optimization of the model obtained by desirability function.

**Table 4.** X-ray fluorescence analysis of sugarcane bagasse.


Therefore, the FTIR was utilized to investigate the functional groups of the sugarcane bagasse (Figure 6). The bands at 3330 and 2890 cm−<sup>1</sup> indicate the presence of the O-H functional group and C–H stretching, respectively [20,38]. The peak at 1632 cm−<sup>1</sup> is assigned to C=O vibrations in hemicellulose [39]. These groups are thought to play a very important role in the process of adsorption [40]. The bands that appeared at wave numbers between 1471 cm−<sup>1</sup> and 1366 cm−<sup>1</sup> are related to C=C–H indicating several bands in cellulose and xylose [41]. The bands appeared at 1241 cm−<sup>1</sup> and 1029 cm−<sup>1</sup> can be attributed to the CH=CH stretching of lignin [39] and C-O stretching in cellulose and hemicellulose [42], respectively. The bands appeared at 640 and 593 cm−<sup>1</sup> in the FTIR spectra are attributed to the vibration of O-H groups out of the plane deformation [43].

#### *3.5. Adsorption Isotherms*

The specific relationship established between the COD remaining in solution (Ce) and the COD adsorbed by sugarcane bagasse (qe) at equilibrium was analyzed by the models of Langmuir and Freundlich. The investigated adsorption isotherms for the COD adsorption on sugarcane bagasse are presented in Figure 7. Table 5 shows the calculated parameters for each of the sorption isotherm models obtained from non-linear regression forms. The best fit of the experimental data was analyzed based on R2, adj-R2, red-*χ*<sup>2</sup> and BIC. The obtained results displayed that the COD adsorption fitted well with the Langmuir isotherm model with the highest adj-*R*<sup>2</sup> of 0.994 and the lowest red-*χ*<sup>2</sup> and BIC values of 78.33 and 25.95, respectively. This result indicates that the adsorption process occurs on a homogeneous surface, and all adsorption sites are identical and energetically equivalent [44]. The maximum adsorption capacity (qmax) of COD onto sugarcane bagasse was determined to be 331.92 mg/g from the Langmuir model.

**Figure 6.** The FTIR spectra of sugarcane bagasse.

**Figure 7.** Isotherm plots for COD removal by sugarcane bagasse.


**Table 5.** Parameters and error function data for sorption isotherm models obtained from non-linear regression forms.

Note: <sup>a</sup> qe (mg/g): mass of adsorbed molecule per unit mass of sugarcane bagasse, Ce (mg/L): concentration of no-adsorbed molecules, qm and KL are constants of the Langmuir model, and KF and n are constants of Freundlich isotherm model.

The adsorption properties of sugarcane bagasse are compared with other adsorbents used for COD sorption reported in the literature, which are given in Table 6. The table indicates that the qmax value obtained in the present study is higher than that of other materials from previous studies. The qmax value found in this study reveals a very good adsorption capacity of the sugarcane bagasse, which falls as a promising adsorbent.

**Table 6.** Comparison of the qmax of COD removal by various adsorbents.


#### *3.6. Kinetic Studies*

Kinetic modeling was undertaken to determine the rate of COD adsorption on the sugarcane bagasse and examine the controlling mechanisms of the adsorption process. The kinetic studies were carried out from 0 to 24 h, and the recorded data were studied with the pseudo-first-order and pseudo-second-order kinetic models. The rate constants values were valued from the non-linear plots and shown in Figure 8 and are also summarized in Table 7. The table indicated that the R2 value obtained from the pseudo-first order kinetic equation was found to be higher than that of pseudo-second order. In addition, the calculated qe value (337.45 mg/g) for the pseudo-first-order model is closer to the experimental value (326.29 mg/g) compared to the qe value (405.9 mg/g) calculated for the pseudo-secondorder model. The pseudo-first-order model also presented the highest adj-R2 (0.967), lowest red-χ<sup>2</sup> (467.41) and BIC (43.01) values. This result advises that the experimental data demonstrated the best fit to the pseudo-first order model, and its applicability also indicates that a physical process might control the sorption process.

**Figure 8.** Kinetic plots for COD removal by bagasse.

**Table 7.** Parameters and error functions data for kinetic models studied obtained from non-linear regression forms.


Note: <sup>b</sup> qe and qt (mg/g) are the amounts of COD removed at equilibrium and at time t, respectively; k1 and k2 are the adsorption rate constants.

#### **4. Conclusions**

In this work, the sugarcane bagasse prepared was utilized as an adsorbent for the elimination of COD from OMW by the adsorption. Sugarcane bagasse characterization was performed by FTIR and X-ray fluorescence. The 21−<sup>5</sup> fractional design associated to response surface methodology was successfully applied to optimize the effects of the operating variables of COD removal from OMW using the prepared adsorbent. The optimal conditions were found to be pH 12, the adsorbent dose of 10 g/L, a stirring speed of 80 rpm, a contact time of 1 h, and a temperature of 60 ◦C with a percentage of removal of 55.07%

and a desirability close to 1. The experimental data are well correlated by the Langmuir isotherm model with an R2 of 0.996, and the maximum sorption capacity of 331.92 mg/g under optimal conditions. The kinetic data of the COD removal process were properly fitted with the pseudo-first-order model instead of pseudo-second-order model.

**Author Contributions:** F.E. carried out the experiments and prepared the draft manuscript. S.E.: and W.B. interpreted and discussed the results, and calculated the statistical parameters. N.B. (Nadia Beniich) analyzed and checked the statistical results. N.B. (Noureddine Barka) verified the analytical methods. C.E.A. and E.L. aided in interpreting the results and worked on the manuscript. M.A. corrected and wrote the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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## *Article* **Comparison of Phenol Adsorption Property and Mechanism onto Different Moroccan Clays**

**Younes Dehmani 1,\* , Dison S. P. Franco 2, Jordana Georgin 2, Taibi Lamhasni 3, Younes Brahmi 4, Rachid Oukhrib 5, Belfaquir Mustapha 6, Hamou Moussout 6, Hassan Ouallal <sup>7</sup> and Abouarnadasse Sadik <sup>1</sup>**


**Abstract:** This study focuses on the removal of phenol from aqueous media using Agouraï clay (Fes-Meknes-Morocco region) and Geulmima clay (Draa Tafilalet region). The characterization of the clay by Fourier Transform Infrared (FTIR) Spectroscopy, X-ray diffraction (XRD), N2 adsorption (BET), Scanning Electron Microscopy (SEM), and Thermogravimetric and differential thermal analysis (DTA/GTA) indicates that it is mainly composed of quartz, kaolinite, and illite. The results showed that raw Clay Agourai (RCA) and raw Clay Geulmima (RCG) adsorbed phenol very quickly and reached equilibrium after 30 min. Thermodynamic parameters reveal the physical nature of the adsorption, the spontaneity, and the sequence of the process. However, the structure and structural characterization of the solid before and after phenol adsorption indicated that the mechanism of the reaction was electrostatic and that hydrogen bonding played an important role in RCG, while kinetic modeling showed the pseudo-second-order model dynamics. The physics-statistics modeling was employed for describing the isotherm adsorption for both systems. It was found that the monolayer model with two different energy sites best describes adsorption irrespective of the system. The model indicates that the receptor density of each clay direct influences the adsorption capacity, demonstrating that the composition of the clay is the main source of the difference. Thermodynamic simulations have shown that the adsorption of phenol is spontaneous and endothermic, irrespective of the system. In addition, thermodynamic simulations show that the RCG could be adsorbed even further since the equilibrium was not achieved for any thermodynamic variable. The strength of this study lies in the determination of the adsorption mechanism of phenol on clay materials and the optimum values of temperature and pH.

**Keywords:** phenol adsorption; different clays; adsorption mechanism; physics-statistics modeling

#### **1. Introduction**

Water is the essential element of life, and the quality and quantity of water on earth ask critical questions of all social actors [1]. Water pollution by various organic and inorganic pollutants is a global problem that requires a solution in the source of pollutants and

**Citation:** Dehmani, Y.; Franco, D.S.P.; Georgin, J.; Lamhasni, T.; Brahmi, Y.; Oukhrib, R.; Mustapha, B.; Moussout, H.; Ouallal, H.; Sadik, A. Comparison of Phenol Adsorption Property and Mechanism onto Different Moroccan Clays. *Water* **2023**, *15*, 1881. https://doi.org/10.3390/w15101881

Academic Editor: Hai Nguyen Tran

Received: 18 April 2023 Revised: 7 May 2023 Accepted: 12 May 2023 Published: 16 May 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

a solution before releasing them into aquatic ecosystems [2]. The pollutants that alter water quality are various and diverse and especially include phenol and phenolic compounds widely used in several industrial and agricultural fields [3]. The remarkably high toxicity of phenol has prompted the services in charge of environmental protection to normalize its concentration in water to reduce its impacts on humans and the environment [4]. Despite the non-high concentrations of these compounds in the aquatic environment, their toxic effects and their impact on the environment are remarkable, and several studies in the literature show the effects of these pollutants on health and the environment [5]. There are numerous techniques for cleaning up water that has been contaminated by organic substances such as degradation, biological treatment, membrane treatment, and catalytic oxidation. However, adsorption is the technique of choice for the removal of phenol [6]. The major advantage of this technique is the possibility of using several types of adsorbent and the simplicity of the process [6]. Clays have proven to be very effective in removing phenol from wastewater [6]. Clay minerals are the most important materials because of their known properties of adsorption and retention of pollutants [7], as well as their ease of modification and/or functionalization [7].

Despite the huge potential of clays, Morocco is still behind in the use of these processes to produce new high-value clay materials that could lead to innovative applications [8–10]. Some studies have focused on adsorption on clays. Agnieszka Gładysz-Płaska used red clay to adsorb uranium [11], and the adsorption of cationic and anionic dyes on clays has been the subject of work by Islem Chaari and collaborators [12]. Meichen Wang was interested in the adsorption of benzo[a]pyrene on modified clays [13]. Several works on the adsorption of dyes on clays and modified clays are mentioned in the work of Abida Kausar [14]. The work of Ali Q. Selim and co-workers [15] aimed at adsorbing phosphates on chemically modified carbonaceous clays. Using a natural clay (kaolinite), Nouria Nabbou tried to remove fluoride from groundwater [16].

There is a special property that characterizes porous solids, especially clay. This characteristic is the ability to adsorb heavy metals and organic matter contained in aqueous solutions, as well as a cationic exchange capacity [15]. This arises mainly due to their natural acidity and their high specific surface. Its performance is affected by several parameters: temperature, pH, and the properties of the adsorbed elements. The Moroccan kaolin clays of the regions of Guelmima (Darra Tafilalet region) and Agourai (Fes Meknes region) are part of this class. They are currently used in the ceramic industry, but they are also potential candidates for use in pollution control.

It is in this general context that this study has as its main objective the valorization and comparison of two Moroccan clays, one from the region of Daraâ Tafilalet and one from the region of Fes Meknes, as natural adsorbents for the retention of phenol from an aqueous solution. This work is divided into two parts. The first part is devoted to the characterization of the raw material. In the second part, we present the adsorption experiments, the methodology adopted and the results obtained, and their interpretations. The general conclusion summarizes the main results of this research work.

#### **2. Materials and Methods**

#### *2.1. Sampling Area*

The clays used in this work come from the town of Agouraï, Fes-Meknes region, and the town of Geulmima, Draa Tafilalet region (Morocco), as shown on the map in Figure 1. The sampled clays were sieved, washed, and dried in the oven for one night at a temperature of 130 ◦C.

**Figure 1.** Geographical site of the clay used.

#### *2.2. Adsorption Batch*

The phenol adsorption experiments were conducted in accordance with the following protocol: a solution of phenol (20 mL) with a concentration that varied between 10 and 500 mg L−<sup>1</sup> was brought into contact with a mass of 0.1 g of solid at a constant temperature (T = 30, 40, and 50 ◦C) for pH = 4 and with a stirring rate of 600 rpm during the adsorption period. At the end of the assay, the mixtures were filtered and analyzed by UV/Vis spectroscopy. The residual concentration was determined based on a UV spectrometer calibration curve/Visible at λ = 270 nm (Shimadzu UV-1240). Equation (1) was used for the determination of the amount adsorbed:

$$\mathbf{Q\_{ads}} = \frac{(\mathbf{C}\_0 - \mathbf{C}\_e)}{\mathbf{m\_{ads}}} \times \mathbf{V\_{sol}} \tag{1}$$

where Qads is the adsorption capacity (mg g), C0 is the initial concentration of the phenol (mg L), Ce is the residual concentration of the phenol (mg L), mads is the mass of adsorbent used (g), and Vsol is the volume of the solution (L).

#### *2.3. Kinetic Modelling*

The experimental results were adjusted to nonlinear models in order to ascertain the adsorption mechanism of phenol on both solids (RCA and RCG). The modeling of the adsorption kinetics was done using the pseudo-first-order and pseudo-second-order models [17]:

$$\mathbf{q}\_{\rm t} = \mathbf{q}\_{\rm q} (1 - \mathbf{e}^{-\rm tk\_{\rm 1}}) \tag{2}$$

$$\mathbf{q}\_{\mathbf{t}} = \mathbf{q}\_{\mathbf{e}}^2 \mathbf{k}\_2 \frac{\mathbf{t}}{\mathbf{q}\_{\mathbf{e}} \mathbf{k}\_2 \mathbf{t} + 1} \tag{3}$$

where qt is the adsorption capacity according to time (mg g<sup>−</sup>1), qe is the adsorption capacity at equilibrium (mg g−1), k1 is the pseudo-first-order kinetics constant (min−1), k2 is the pseudo-second-order kinetic constant (g mg−<sup>1</sup> min<sup>−</sup>1), and t is the time (min).

#### *2.4. Isotherm Modeling through the Physic-Statistics Approach*

In this study, the adsorption of phenol onto several adsorbents was compared using a physical-statistical (Phys-Stat) modeling approach (RCA and RCG). The use of the grand canonical ensemble serves as the foundation for the Phys-stat models. which defines systems that, at a constant temperature, can exchange particles with their environment.

#### 2.4.1. Monolayer Model with Single Energy Site (MLO)

The most commonly employed model is the monolayer model with one energy site (MLO). This model takes into consideration that a variable number of phenol molecules (n, dimensionless) is adsorbed in one type of receptor site with energy (−ε1), reflecting directly in the quantity of adsorbed molecules, represented by the receptor density (Nm, mg g<sup>−</sup>1), according to Equation (4) [18]:

$$\mathbf{q}\_{\mathbf{e}} = \frac{\mathbf{n} \mathbf{N}\_{\mathbf{m}}}{1 + \left(\frac{\mathbf{C}\_1 \, ^{\prime} \mathbf{C}}{\mathbf{C}\_{\mathbf{e}}}\right)^{\mathbf{n}}} = \frac{\mathbf{q}\_{\mathbf{m}}}{1 + \left(\frac{\mathbf{C}\_1 \, ^{\prime} \mathbf{c}}{\mathbf{C}\_{\mathbf{e}}}\right)^{\mathbf{n}}} \tag{4}$$

where C1/2 is the concentration at half-saturation (mg L−1) and qm is the adsorption capacity at saturation (mg g<sup>−</sup>1).

#### 2.4.2. Monolayer Model with Two Energy Sites (MLT)

When considering that a variable number of phenol molecules can be adsorbed onto two energetically different sites (−ε<sup>1</sup> and −ε2) the monolayer model with two energy sites (MLT) is obtained. In this case, a different number of phenol molecules (n1 and n2, dimensionless) are adsorbed onto different receptors sites with different densities (Nm1 and Nm2, mg g<sup>−</sup>1), according to Equation (5) [19]:

$$\mathbf{q}\_{\mathbf{e}} = \frac{\mathbf{n}\_1 \mathbf{N}\_{\mathbf{m}1}}{1 + \left(\frac{\mathbf{C}\_1}{\mathbf{C}\_\mathbf{e}}\right)^{\mathbf{n}\_1}} + \frac{\mathbf{n}\_2 \mathbf{N}\_{\mathbf{m}2}}{1 + \left(\frac{\mathbf{C}\_2}{\mathbf{C}\_\mathbf{e}}\right)^{\mathbf{n}\_2}} = \frac{\mathbf{q}\_{\mathbf{m}1}}{1 + \left(\frac{\mathbf{C}\_1}{\mathbf{C}\_\mathbf{e}}\right)^{\mathbf{n}\_1}} + \frac{\mathbf{q}\_{\mathbf{m}2}}{1 + \left(\frac{\mathbf{C}\_2}{\mathbf{C}\_\mathbf{e}}\right)^{\mathbf{n}\_2}}\tag{5}$$

where C1 and C2 are the concentrations at half-saturation of the first and second receptor sites (mg L−1), respectively, and qm1 and qm2 are the adsorption capacity at saturation of the first and second receptor sites (mg g<sup>−</sup>1), respectively.

#### 2.4.3. Double-Layer Model with One Energy Site (DLO)

Another possibility is the formation of dual layers. This hypothesis emerges from the consideration that the phenol molecules (n, dimensionless) can form a dual layer of molecules with a single energy of adsorption (−ε1) with a single density of receptor site (Nm, mg g<sup>−</sup>1) according to Equation (6) [20]:

$$\mathbf{q}\_{\mathbf{e}} = \mathbf{n} \mathbf{N}\_{\mathbf{m}} \frac{\left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1/2}}\right)^{\mathbf{n}} + 2\left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1/2}}\right)^{2\mathbf{n}}}{1 + \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1/2}}\right)^{\mathbf{n}} + \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1/2}}\right)^{2\mathbf{n}}} = \mathbf{q}\_{\mathbf{m}} \frac{\left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1/2}}\right)^{\mathbf{n}} + 2\left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1/2}}\right)^{2\mathbf{n}}}{1 + \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1/2}}\right)^{\mathbf{n}} + \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1/2}}\right)^{2\mathbf{n}}} \tag{6}$$

where C1/2 (mg L−1) is the concentration at half-saturation and qm is the adsorption capacity at saturation (mg g<sup>−</sup>1).

#### 2.4.4. Double-Layer Model with Two Energy Sites (DLT)

It is also possible that the phenol molecules (n, dimensionless) can form a double-layer, with each layer having different adsorption energy (–ε<sup>1</sup> and –ε2), with the same receptor sites having the same density (Nm, mg g<sup>−</sup>1), here presented by Equation (7) [21]:

$$\mathbf{q}\_{\mathbf{e}} = \mathbf{n} \mathbf{N}\_{\mathbf{m}} \frac{\left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1}}\right)^{\mathbf{n}} + 2\left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{2}}\right)^{2\mathbf{n}}}{1 + \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1}}\right)^{\mathbf{n}} + \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{2}}\right)^{2\mathbf{n}}} = \mathbf{q}\_{\mathbf{m}} \frac{\left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1}}\right)^{\mathbf{n}} + 2\left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{2}}\right)^{2\mathbf{n}}}{1 + \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1}}\right)^{\mathbf{n}} + \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{2}}\right)^{2\mathbf{n}}} \tag{7}$$

where C1 and C2 are the half-saturation concentration (mg L<sup>−</sup>1), and qm is the adsorption capacity at saturation (mg g<sup>−</sup>1).

#### 2.4.5. Multilayer Model (MM)

Last, there is the possibility of forming a multilayer of adsorption. This model considers that the anchorage of the phenol molecules (n, dimensionless) depends on two energies, one corresponding to the first layer (–ε1) and the other to all layers beyond the first (–ε2), where the energy of adsorption of the first layer is always higher than the other layers. Similar to the other described model, the multilayer model considers only one density receptor site and the same number of molecules per layer [22]. The model is described according to the following Equations:

$$\mathbf{q}\_{\rm e} = \mathbf{n} \mathbf{N}\_{\rm m} \left( \frac{\mathbf{F}\_1 + \mathbf{F}\_2 + \mathbf{F}\_3 + \mathbf{F}\_4}{\mathbf{G}} \right) = \mathbf{q}\_{\rm m} \left( \frac{\mathbf{F}\_1 + \mathbf{F}\_2 + \mathbf{F}\_3 + \mathbf{F}\_4}{\mathbf{G}} \right) \tag{8}$$

$$\mathbf{F}\_{1} = -\frac{2\left(\frac{\mathbf{C}\_{\text{e}}}{\mathbf{C}\_{1}}\right)^{2\mathbf{n}}}{\left(1 - \left(\frac{\mathbf{C}\_{\text{e}}}{\mathbf{C}\_{1}}\right)^{\mathbf{n}}\right)} + \frac{\left(\frac{\mathbf{C}\_{\text{e}}}{\mathbf{C}\_{1}}\right)^{\mathbf{n}}\left(1 - \left(\frac{\mathbf{C}\_{\text{e}}}{\mathbf{C}\_{1}}\right)^{\mathbf{n}}\right)}{\left(1 - \left(\frac{\mathbf{C}\_{\text{e}}}{\mathbf{C}\_{1}}\right)^{\mathbf{n}}\right)^{2}}\tag{9}$$

$$\mathbf{F}\_2 = \frac{2\left(\frac{\mathbb{C}\_e}{\mathbb{C}\_1}\right)^{\mathrm{n}} \left(\frac{\mathbb{C}\_e}{\mathbb{C}\_2}\right)^{\mathrm{n}} \left(1 - \left(\frac{\mathbb{C}\_e}{\mathbb{C}\_2}\right)^{\mathrm{nN}\_2}\right)}{\left(1 - \left(\frac{\mathbb{C}\_e}{\mathbb{C}\_2}\right)^{\mathrm{n}}\right)}\tag{10}$$

$$\mathbf{F}\_3 = \frac{\left(\frac{\mathbb{C}\_e}{\mathbb{C}\_1}\right)^{\mathrm{n}} \left(\frac{\mathbb{C}\_e}{\mathbb{C}\_2}\right)^{\mathrm{2n}} \left(\frac{\mathbb{C}\_e}{\mathbb{C}\_2}\right)^{\mathrm{nN}\_2} \mathrm{N}\_2}{\left(1 - \left(\frac{\mathbb{C}\_e}{\mathbb{C}\_2}\right)^{\mathrm{n}}\right)} \tag{11}$$

$$\mathbf{F}\_4 = \frac{\left(\frac{\mathbf{C}\_e}{\mathbf{C}\_1}\right)^{\mathrm{n}} \left(\frac{\mathbf{C}\_e}{\mathbf{C}\_2}\right)^{2\mathrm{n}} \left(1 - \left(\frac{\mathbf{C}\_e}{\mathbf{C}\_2}\right)^{\mathrm{nN}\_2}\right)}{\left(1 - \left(\frac{\mathbf{C}\_e}{\mathbf{C}\_1}\right)^{\mathrm{n}}\right)^2} \tag{12}$$

$$\mathbf{G} = \frac{\left(1 - \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1}}\right)^{2\mathbf{n}}\right)}{\left(1 - \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1}}\right)^{\mathbf{n}}\right)} + \frac{\left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{1}}\right)^{\mathbf{n}}\left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{2}}\right)^{\mathbf{n}}\left(1 - \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{2}}\right)^{\mathbf{n}\mathbf{N}\_{2}}\right)}{\left(1 - \left(\frac{\mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_{2}}\right)^{\mathbf{n}}\right)}\tag{13}$$

where Nm is the density receptor of the receptor site (mg g<sup>−</sup>1), N2 is the number of formed layers, C1 and C2 are the half-saturation concentration (mg L<sup>−</sup>1), and qm is the adsorption capacity at saturation (mg g<sup>−</sup>1).

#### *2.5. Parameter Estimation and Model Evaluation*

The steric parameters were estimated using Matlab scripting programming. The builtin Matlab functions were utilized for this: particleswarm for the initial guess estimation, and lsqnonlin or nlinfit for incorrect particleswarm estimation. The difference between *lsqnonlin* and *nlinfit* is in the restrictions, where the first is used for a limited boundary and the second performs optimization for an undefined boundary. For the model evaluation, the coefficient of correlation (R2), adjusted coefficient of determination (R2 adj), minimum squared error (MSR, (mg g−1) 2), average relative error (ARE, %), and Bayesian Criterion Indicator (BIC) were employed [22]:

$$\mathbf{R}^2 = 1 - \frac{\sum\_{i=1}^n \left(\mathbf{q}\_{\text{exp}} - \mathbf{q}\_{\text{pred}}\right)^2}{\sum\_{i=1}^n \left(\mathbf{q}\_{\text{exp}} - \dot{\mathbf{q}}\_{\text{exp}}\right)^2} \tag{14}$$

$$\text{ARE} = \frac{100\%}{\text{n}} \sum\_{i=1}^{\text{n}} \left| \frac{\mathbf{q}\_{\text{exp}} - \mathbf{q}\_{\text{pred}}}{\mathbf{q}\_{\text{exp}}} \right| \tag{15}$$

$$\text{MSR} = \frac{1}{\text{n} - \text{p}} \sum\_{i=1}^{\text{n}} \left( \mathbf{q}\_{\text{exp}} - \mathbf{q}\_{\text{pred}} \right)^{2} \tag{16}$$

$$\text{BIC} = \text{nLn}\left(\frac{\text{RSS}}{\text{n}}\right) + \text{pLn}(\text{n}) \tag{17}$$

where qexp is the experimental adsorption capacity at the equilibrium (mg g−1) and qpred is the predicted adsorption capacity at the equilibrium (mg g−1), n is the number of experimental data points, and p is the number of parameters used in the model.

#### *2.6. Structure Characterization*

First, an "Axion" type X-ray fluorescence spectrometer with a dispersion of 1 kW wavelength was used to measure the X-ray fluorescence. The UATRS laboratory and CNRST in Rabat, Morocco, performed this chemical analysis. Then, using an X'PERT MPD-PRO wide-angle X-ray powder diffractometer equipped with a diffracted beam monochromator and Ni-filtered CuK radiation (λ = 1.5406), X-ray diffraction (XRD) patterns were captured. With a counting period of 2.0 s and increments of 0.02◦, the 2θ angle was scanned from 4◦ to 30◦. Then, using a Fourier transform infrared spectrometer, the Fourier transforms infrared (FTIR) spectra of RCG and CCG were characterized (VERTEX70). The samples were made in a conventional manner on KBr discs from extremely well-dried mixes containing about 4% (*w*/*w*). FTIR spectra between 4000 and 400 cm−<sup>1</sup> were captured. Using LABSYS/Evo thermal, Thermogravimetric Analysis/Differential Thermal Analysis (TGA/DTA) studies were performed in an air environment. The samples were heated linearly (T = T0 + t) at a rate of 20 ◦C/min from ambient to 600 ◦C. In order to determine the textural characteristics, BET Nitrogen adsorption measurements were acquired using Micromeritics ASAP 2010. SEM images were acquired using a Quanta 200 EIF microscope equipped with standard secondary electron (SE) and backscattered electron (BSE) detectors. The electron beam is produced by a conventional tungsten electron source, which can resolve features as small as 3 nm under optimal operating conditions. The microscope is equipped with an Oxford Inca energy dispersive X-ray (EDX) system for elemental chemical analysis.

#### **3. Results**

#### *3.1. X-ray Fluorescence*

Our findings showed that, of the clays under study, silica, alumina, and calcium oxide made up around 66% of the composition of the RCG clays and 50 percent of the RCA clays, respectively. The concentrations of alkali and alkaline earth metals were lower in both clay solids (Table 1). This chemical analysis showed a 10.5% lower alumina content compared

to the alumina content of fire clay: 45% [15,23]. Moreover, the weight of calcite in the clay of the Fes Meknes region is higher than that of Geulmima. The presence of calcite reduces the shrinkage of the agglomerate.

**Table 1.** Chemical composition of the studied clays.


#### *3.2. X-ray Patterns of RCA and RCG*

The diffractograms of RCG and RCA are shown in Figure 2. The diffractogram of RCG shows that Quartz (Q), Kaolinite (K), and Illite make up the majority of the clay (I). Conversely, the XRD diffractogram of RCA shows that the composition consists of kaolinite (Al2Si2O5(OH)4), illite [(K, H3O) Al2Si3AlO10(OH)2], quartz (SiO2), and calcite (CaCO3). However, a comparison of the diffraction patterns shows the existence of two strong peaks, the first of which is connected to quartz and the other to calcite, indicating that this clay is heterogeneous [15,24,25].

**Figure 2.** XRD patterns of RCA and RCG.

#### *3.3. FTIR Spectra of RCA and RCG*

Figure 3 shows the FTIR spectra of RCA and RCG. Both spectra show a broad absorption band around 3425 cm−<sup>1</sup> due to the stretching and bending vibrations, respectively, of adsorbed H2O [26]. It is possible to assign the bands at 3649, 3690, and 3650 cm−<sup>1</sup> to the hydroxyl group's stretching vibration in various environments (Al, AlOH), (Al, MgOH), or (Al, FeOH) [27]. For the RCA clay, a band located at 1380 cm−<sup>1</sup> indicates the presence of calcite, while the bands detected at 2870, 1800, 1435, 870, and 715 cm−<sup>1</sup> are attributable to the deformation and elongation vibrations of calcite (CaCO3) [28]. Vibrational distortion of the Si-O-Al bond is responsible for the band seen at 800 cm−1. The bands at 470 and

520 cm−<sup>1</sup> are related to the deformation vibrations of the Si-O bond in quartz, while the band at 1030 cm−<sup>1</sup> is associated with the elongation vibration of the Si-O-Si bond in kaolinite or quartz [29]. The bands at 420 cm−1, 470 cm−1, and 525 cm−<sup>1</sup> correspond to the deformations of Si-O-Fe, Si-O-Mg, and Si-O-Al. Due to the Si-O stretching, a band seen at 1020 cm−<sup>1</sup> is appropriate [28]. The bands centered at 985, 836, 797, 674, and 508 cm−<sup>1</sup> can be attributed to the vibrational deformation of Al-OH-Al, Si-O-Al / Al-Mg-OH, cristobalite, Si-O-Mg, and Mg-OH bonds, respectively [30]. In addition to the Al-OH-Al strain vibration bonds, the band centered at 915 cm−<sup>1</sup> is also attributable to the presence of kaolinite. There are quartz-corresponding absorption bands at 797 and 779 cm−<sup>1</sup> [31].

**Figure 3.** FTIR spectra of RCA and RCG.

#### *3.4. TGA/TDA*

Since both solids only experience a mass loss due to water adsorbed on their surfaces, the TGA curves (Figure 4) demonstrate the remarkable thermal stability of the two clays up to 600 ◦C. The DTG thermogram for the Geulmima clay (RCG) does, in fact, show the existence of an endothermic peak at 120 ◦C that corresponds to the dehydration and mass loss (1.29%) of the physisorbed water. The thermogram of Agourai clay (RCA) shows that the global mass measured is evaluated at 27% by an endothermic peak at 75 ◦C and 128 ◦C. This loss corresponds to the elimination of surface water. Thermal analysis reveals that the two clays under study vary in that there is an exothermic peak between 280 and 380 ◦C. Another endothermic peak between 450 and 550 ◦C, which is accompanied by a minor loss of mass, has been linked to the dehydroxylation of the raw clay for the RCA. This peak has been linked to the removal of organic materials [32,33].

**Figure 4.** TGA/DTA Thermograms of RCA and RCG.

#### *3.5. N2 Adsorption/Desorption Isotherm of RCA and RCG*

The nitrogen adsorption/desorption method (BET method by Breunuer, Emet, and Teller) is very important in the determination of the textural properties of solids. The textural parameters influence the catalytic and adsorptive capacity of the materials in the treatment of liquid pollutants. Based on the International Union of Pure and Applied Chemistry (IUPAC)'s recommended classification system for physical adsorption isotherms, the isotherms of the clays belong to the type IV isotherm (Figure 5) with a hysteresis loop of type H3 [34]. There are platforms in the region where the P/P0 value is higher than the strongly increasing isotherms. These lines indicate that the samples contain macropores more than 50 nm in diameter [35]. Based on the data Table 2, the clay of the region of Fez Meknes (RCA) has a larger surface than the clay of Draa Tafilalet (RCG).even at the level of pore volume and pore diameter, the clay of Agourai is better than that of Geulmima

**Figure 5.** N2 adsorption/desorption isotherms of RCA and RCG.


**Table 2.** Textural characteristics of RCA and ACA.

#### *3.6. SEM*

The SEM micrographic images (Figure 6) of both samples show the porous nature of our materials, which facilitates the penetration of the phenol molecules. Moreover, these images confirm that the structure of the clays is formed by a relatively homogeneous and compact clay matrix. The EDX spectra (Figure 7) show the presence of the main elements such as silica, aluminum, and oxygen, which confirms the validity of the X-ray fluorescence analysis.

**Figure 6.** SEM images of RCA and RCG.

**Figure 7.** EDX data of RCA and RCG.

#### *3.7. Point of Zero Charge*

In the phenomenon of phenol adsorption, the determination of the point of zero charges is important because of the information on this point on the charge of the adsorbent surface and the intervals of change of this charge. The pH of zero charge point pHpzc of the RCG and RCA clays are 7.84 and 8.60, respectively (see Supplementary Materials Figures S1 and S2). Thus, for pH values higher than the pH of the points of null charge of the solids, the surface is negatively charged. At lower pH, the surface is positively charged. The adsorption of

phenol is more significant in the case of positively charged surfaces, so we work in the pH< pHpzc of the solids. A difference between the two solids at the pHpzc level can affect the adsorption capacity.

#### *3.8. Determination of CEC*

The cation exchange capacity was determined using the method of Delphine Aran [36]. Solids have a very high capacity compared to other types of clays [35]. The CEC of RCA is about 10.6 meq/100 g, while that of RCG is 16.64 meq/100 g. Compared with RCA, RCG has a higher switching capacity. This difference is evidenced by the percentages of elements that make up the two materials.

#### *3.9. pH Effect*

The pH of the solution is an important and specific factor in any liquid pollutant adsorption study as it affects the structure of the adsorbent and adsorbate as well as the adsorption mechanism. Therefore, it is logical to know the adsorption efficiency at different pH values to determine the optimal pH for adsorption. The literature gives two cases of the influence of pH on the adsorption of phenol [37]. We can see that in the acidic state, the positive charge on the surface of the adsorbent is dominant, so there is strong static electricity between the adsorbate and the adsorbent on the surface charge. In the ground state, the dramatic drop in adsorption capacity is evidenced by the nature of the predominantly positive charge on the solid surface [38].

For RCG, the adsorption capacity of phenol decreases with a slight increase in pH. The equilibrium adsorption capacity is 2.88 mg. g−<sup>1</sup> and 2.54 for pH = 4 and 11, respectively. The Agourai clay showed an important decrease in adsorption capacity from pH = 4 to pH = 11, from 1.2 to 0.8 mg g<sup>−</sup>1, respectively (Figure 8). These results can be explained by the zero-charge pH parameter, which provides information about the dominant charge on the surface and can thus be demonstrated and interpreted in terms of the properties of the materials used. This can be explained by the fact that in the ground state (pH > pHpcn), the predominant charge on the adsorbent surface is negative, which reduces the adsorption of the same charged phenoxide. In the acidic state, the positive charge on the surface of the adsorbent is dominant, so there is a relatively high electrostatic attraction between the positive charge on the surface of the adsorbent and the negative charge of the formed phenoxide, which promotes the adsorption. In short, heterogeneous composition leads to different interactions between adsorbate and adsorbent.

**Figure 8.** Effect of pH on the phenol adsorption onto RCA and RCG.

#### *3.10. Adsorption of Phenol* 3.10.1. Adsorption Kinetics

To evaluate the potential application of the samples in wastewater treatment, the variation in the adsorbed amounts of phenol on the two solids as a function of time and temperature is presented in Figure 9. The adsorbed amount in both solids increases rapidly up to 30 min, and then gradually increases up to 180 min. After this time, it remains almost unchanged at 360 min. The increase in the adsorbed quantity as a function of the temperature can be explained by the effect of the heat on the space between the particles of the solids, a dilatation of the latter imparts fast mobility to the phenol molecules, so an effect can be noticed on the molecules of the phenol by the increase in temperature, which accelerates the fixation on the surface of the clay materials. The amounts of phenol adsorbed by the RCG clay are 1.84, 2.43, and 3.52 mg g−<sup>1</sup> for temperatures 30 ◦C, 40 ◦C, and 50 ◦C, respectively. While the amount adsorbed by the RCA clay is 1.39, 2.10, and 2.71 mg g−1, respectively, for the same temperatures. The adsorption capacity was better for RCG. This property is dependent on the structural and textural properties of this clay, particularly the zero charge point, which gives an important dominance of positive charges.

**Figure 9.** Adsorption kinetics of phenol onto RCA and RCG at different temperatures.

Understanding the adsorption kinetics is necessary to study adsorption because the process can predict the rate of adsorption and explain the mechanism of adsorption. Pseudofirst-order and pseudo-second-order kinetics were used to study the adsorption process of phenol on clay materials. Table 3 lists the parameters of the pseudo-first-order (Equation (2)) and pseudo-second-order (Equation (3)) model equations, and the model representations are shown in Figure 10 [39]. The results show that the adsorption kinetics are described by pseudo-second-order kinetics with a coefficient of determination close to 1 (Table 3).

**Table 3.** Kinetic parameters of linear and nonlinear modeling of phenol adsorption at different temperatures onto RCA and RCG.


**Figure 10.** Adsorption kinetics with nonlinear models of pseudo-first-order and pseudo-second-order of phenol (C0 = 5.10−<sup>4</sup> M) onto RCA and RCG at pH = 4.

#### 3.10.2. Adsorption Isotherms and Physical Statistical Interpretations

The experimental data and the physical statistical prediction are shown in Figure 11. Starting with the experimental data, it was found that both Moroccan clays (RCA and RCG) were able to adsorb the phenol, showing improvements according to the evolution of the system temperature. For the selection of the most suitable Phys-Stat model, the statistical indicators described in Section 2.6 and summarized in Table 4 were employed. Taking into consideration the results obtained from the statistical indicators, the first model to be eliminated is the MM, which corresponded to the Multilayer model. The failure to present a good correlation coefficient (R2), clearly indicates that the phenol molecules are not able to perform multilayer adsorption. The monolayer and dual-layer model present good statistical indicators. However, aiming to obtain the most adequate model, the BIC was used as an evaluation parameter. The BIC indicates that the monolayer model with two different energy sites (MLT) is the most adequate model for describing the adsorption of phenol onto the RCA and RCG clays. In addition to the statistical indicators, the steric parameters obtained for the other models, besides the MLT, do not present coherence. In other words, in cases where the concentration at half-saturation should be increasing, it diminishes or presents fluctuation. Overall the MLT indicates that the different number of phenol molecules (n1 and n2) are adsorbed onto two different energetic sites (–ε<sup>1</sup> and –ε2), which leads to different receptor densities (Nm1 and Nm2). Thus, taking into consideration the MLT, the steric parameters are further discussed aiming to give insight into the phenol adsorption mechanism.

The number of adsorbed molecules per site is also known as the stereographic coefficient, which governs adsorption. The nature of the value also indicated how the phenol molecules are attached to the surface of the RCA and RCG. When the n values are below 1, the molecules are adsorbed in a parallel way. While, for values above 1, the molecules are attached on the surface in a perpendicular or non-parallel fashion [40]. In addition, the anchorage number (na = 1/n) represents the number of sites occupied by one molecule. The evolution of the number of adsorbed molecules per site according to the temperature and systems is presented in Figure 12. The first notable aspect is that the RCA and RCG clays have different numbers of molecules per site. For the RCA clay, the number of molecules per site is above 1, indicating that all molecules are adsorbed in a perpendicular way to the surface, irrespective of the receptor site. In addition, in all cases, the number of molecules

in site 1 is higher than in site 2 (n1 > n2), meaning that the phenol has a preference for the receptor site 1. In terms of anchor numbers, the estimated variation in phenol molecules is 0.02848 < na1 < 0.03847 for receptor site 1 and 0.1355 < na2 < 0.6219 for receptor site 2. This means that the phenol molecules occupy less than one full position in each case. This could be due to affinity or steric effects. In other words, the RCA clay has a deficit of receptor sites with a high affinity for the phenol molecules. The change in the number of molecules per site for the phenol/RCG is depicted in Figure 12B. In this case, the number of molecules per site is 10 times lower in comparison with the phenol/RCA. There are different possibilities to explain this difference between each material: (i) due to the density of receptor sites, (ii) due to textural proprieties, however, this could not be the reason due to the RCA having a higher specific surface area than the RCG, or (iii) the different compositions [41–43]. According to Table 1, the materials present differences in composition, with the RCG clay presenting higher quantities of silicon dioxide (SiO2). Works in the literature reported that silicon dioxide directly influences the quantities of phenol adsorbed [6,44,45]. In a similar way, it is also possible to estimate the anchorage number for the phenol/RCG system; where na1 ranged from 0.8451 to 1.410 and 0.1824 to 0.8331 for na2, according to the temperature. This indicates that the phenol molecules tend to bind to more receptor sites of type one as the system temperature increases. As for receptor site type two, the phenol molecules tend to need less than one receptor site per molecule.

**Figure 11.** Phenol adsorption isotherms for the RCA (**A**) and RCG (**B**), lines are the model value predictions.


**Table 4.** Statistical indicators according to the model and adsorbent.

The change in receptor density according to the evolution of the temperature and the system, phenol/RCA or phenol/RCG, is depicted in Figure 13. Starting with the RCA, it is possible that as the temperature of the system increases, the density of receptor two increases, while that of receptor one remains almost constant at around 0.5. This indicates that the affinity of receptor two is not dependent on the system temperature and that the increase in the adsorption capacity is due to receptor site two, the density of which increases. This effect can be related to two different causes: (i) a compensation regarding the number of molecules per site, in particular of receptor site one, where it achieved a higher value (above 10); (ii) the adsorbent material tends to change according to the temperature, facilitating the affinity between the phenol molecules and the RCA [46]. The density of the receptor sites for the phenol/RCG was found to observe more typical behavior, with the densities for both receptor sites tending to increase with the temperature. At all temperatures, the density of receptor site one (Nm1) is higher than that of receptor site two (Nm2), indicating that receptor one has a high affinity and thus, facilitates the adsorption of phenol. In addition, the minor decrease in the number of the adsorbed phenol molecules (n1) direct reflects the behavior of the receptor density Nm1, where the value increases, indicating a higher number of molecules are attached to this site [47].

**Figure 12.** Evolution of the number of molecules per site parameters according to the temperature and system, RCA (**A**) and RCG (**B**).

**Figure 13.** Receptor site density changes according to the temperature and system, RCA (**A**) and RCG (**B**).

From the half-concentration saturation parameters, it is possible to determine the adsorption energy according to the receptor site and system, as shown in Figure 14. The first aspect found is that the adsorption energy is positive for both systems, indicating endothermic adsorption based on physical interactions since the values were below 40 kJ mol−1. For the phenol/RCA, it was found that receptor one has higher adsorption energy than receptor two, ΔE1 > ΔE2. In other words, receptor one has a higher affinity for the phenol. At first glance, this may seem to be in contradiction with the receptor densities, however, this may indicate that lower quantities of the receptor of type one may be present on the surface. For the phenol/RGC system, the energy of receptor one is also higher than receptor two. The minor difference is that, as the temperature evolves, a less them 1% decrease in the energy for the receptor site one occurs. The main explanation for this is the minor change that occurs in the number of molecules for receptor one [47]. In addition, these changes in the adsorption energy do not indicate a change in the nature of the adsorption, thus being endothermic for all the temperatures tackled in this study.

**Figure 14.** Estimated adsorption energy change according to the temperature and system, RCA (**A**) and RCG (**B**).

3.10.3. Application of the Stat-Phys Model with the Thermodynamic Potential Functions

Thermodynamic potential functions are quantities that are used to describe the state of a thermodynamic system and the energy exchanges that can occur within it. As it is possible to associate the grande canonical partition function to obtain the thermodynamic potential function of the configurational entropy (SA, kJ mol−<sup>1</sup> K<sup>−</sup>1), Gibbs free energy for adsorption (GA, kJ mol<sup>−</sup>1), and internal energy of adsorption (EI, kJ mol−1. The definition of each thermodynamic variable is given by the following Equations [48]:

$$\frac{\mathbf{S\_{\dot{a}}}}{\mathbf{k\_{B}}} = \ln \left( Z\_{\rm gc} \right) - \beta \frac{\partial}{\partial \beta} \ln \left( Z\_{\rm gc} \right) \tag{18}$$

$$\mathbf{G}\_{\mathbf{a}} = \mu \mathbf{Q}\_{\mathbf{0}} \tag{19}$$

$$\mathcal{E}\_{\rm int} = \frac{\mu}{\beta} \left( \frac{\partial}{\partial \mu} \ln \left( \mathcal{Z}\_{\rm gc} \right) \right) - \frac{\partial}{\partial \beta} \ln \left( \mathcal{Z}\_{\rm gc} \right) \tag{20}$$

where Zgc corresponds to the total grand canonical partition function, μ corresponds to the translational chemical potential of the phenol molecule (kJ mol<sup>−</sup>1), β corresponds to 1/kBT, kB corresponds to the Boltzmann constant, and T is the temperature of the system.

Taking into consideration that the MLT was the best Stat-Phys model to represent both systems, the thermodynamic parameters can be obtained according to Equations (21)–(23). The derivation of the Equations can be found elsewhere [47,49].

$$\frac{\mathbf{S\_{a}}}{\mathbf{k\_{B}}} = \begin{cases} \mathbf{N\_{m1}} \left[ \ln \left( 1 + \left( \frac{\mathbf{C\_{e}}}{\mathbf{C\_{1}}} \right)^{\mathbf{n}} \right) - \ln \left( 1 + \left( \frac{\mathbf{C\_{e}}}{\mathbf{C\_{1}}} \right)^{\mathbf{n}} \right) \frac{\left( \frac{\mathbf{C\_{e}}}{\mathbf{C\_{1}}} \right)^{\mathbf{n}}}{1 + \left( \frac{\mathbf{C\_{e}}}{\mathbf{C\_{1}}} \right)^{\mathbf{n}}} \right] \\\\ + \mathbf{N\_{m2}} \left[ \ln \left( 1 + \left( \frac{\mathbf{C\_{e}}}{\mathbf{C\_{2}}} \right)^{\mathbf{n}} \right) - \ln \left( 1 + \left( \frac{\mathbf{C\_{e}}}{\mathbf{C\_{2}}} \right)^{\mathbf{n}} \right) \frac{\left( \frac{\mathbf{C\_{e}}}{\mathbf{C\_{2}}} \right)^{\mathbf{n}}}{1 + \left( \frac{\mathbf{C\_{e}}}{\mathbf{C\_{2}}} \right)^{\mathbf{n}}} \right] \end{cases} \tag{21}$$

$$\mathbf{G}\_{\mathbf{a}}\boldsymbol{\beta} = \ln\left(\frac{\mathbf{C}\_{\mathbf{e}}}{\left(\frac{2\pi\mathbf{m}}{\hbar^{2}\boldsymbol{\beta}}\right)^{3/2}}\right)\left(\frac{\mathbf{Q}\_{\mathbf{m}1}}{1+\left(\frac{\mathbf{C}\_{1}}{\mathbf{C}\_{\mathbf{e}}}\right)^{\mathbf{n}}}+\frac{\mathbf{Q}\_{\mathbf{m}2}}{1+\left(\frac{\mathbf{C}\_{2}}{\mathbf{C}\_{\mathbf{e}}}\right)^{\mathbf{n}}}\right) \tag{22}$$

$$\mathbf{E}\_{\rm int} = \begin{cases} \begin{array}{c} \mathrm{N}\_{\rm m1} \left[ \mu \frac{\left( \frac{\mathbf{C}\_{\rm e}}{\mathbf{C}\_{1}} \right)^{\mathrm{n}}}{1 + \left( \frac{\mathbf{C}\_{\rm e}}{\mathbf{C}\_{1}} \right)^{\mathrm{n}}} \frac{1}{\mathsf{B}} \ln \left( 1 + \left( \frac{\mathbf{C}\_{\rm e}}{\mathbf{C}\_{1}} \right)^{\mathrm{n}} \right) \frac{\left( \frac{\mathbf{C}\_{\rm e}}{\mathbf{C}\_{1}} \right)^{\mathrm{n}}}{1 + \left( \frac{\mathbf{C}\_{\rm e}}{\mathbf{C}\_{1}} \right)^{\mathrm{n}}} \right] \\ + \mathrm{N}\_{\rm m2} \left[ \mu \frac{\left( \frac{\mathbf{C}\_{\rm e}}{\mathbf{C}\_{2}} \right)^{\mathrm{n}}}{1 + \left( \frac{\mathbf{C}\_{\rm e}}{\mathbf{C}\_{2}} \right)^{\mathrm{n}}} - \frac{1}{\mathsf{B}} \ln \left( 1 + \left( \frac{\mathbf{C}\_{\rm e}}{\mathbf{C}\_{2}} \right)^{\mathrm{n}} \right) \frac{\left( \frac{\mathbf{C}\_{\rm e}}{\mathbf{C}\_{2}} \right)^{\mathrm{n}}}{1 + \left( \frac{\mathbf{C}\_{\rm e}}{\mathbf{C}\_{2}} \right)^{\mathrm{n}}} \end{array} \tag{23}$$

#### 3.10.4. Interpretations of the Potential Function

The simulations of the change in the configuration entropy according to the phenol equilibrium concentration and the systems are depicted in Figure 15. The simulations take into consideration the maximum concentration equilibrium obtained from the isotherms, meaning around 500 mg L−1, for a fixed value. The first aspect to be observed is that the configuration entropy for the phenol/RCA presents two entropic peaks. The first is at around 5 to 7 mg L<sup>−</sup>1, which corresponds to the concentration at half-saturation for receptor site two. After that, a second minor entropic peak occurs, corresponding to the receptor site one. After that, the entropic change diminishes until achieving equilibrium. The second aspect is regarding the phenol/RCG system, where, at first glance, the simulation does not show the entropic equilibrium and shows one single entropic peak for all temperatures. Regarding the entropic equilibrium, further simulation indicates that it is only reached after 1500 mg L<sup>−</sup>1. This indicates that the adsorption could occur beyond the concentration investigated in this work. Regarding the single entropic peak, the main explanation is that receptor sites one and two present close half-saturation concentrations, meaning that the adsorption occurs at the same equilibrium concentration. Last, regarding the temperature effect, the results are in agreement with the endothermic nature, in which the temperature increases both the adsorption capacity and the entropic behavior.

The simulation of the Gibbs free energy according to the equilibrium concentration and temperature system is shown in Figure 16. The first observation is that the Gibbs free energy is negative for all phenol equilibrium concentrations, temperatures, and systems, indicating that the adsorption of phenol is spontaneous at any given point. Regarding the different profiles of each simulation, the explanation goes hand in hand with the entropic behavior. In the first case, phenol/RCA displays a ladder-type profile. This profile is related to the different receptor sites' half-saturation concentrations, where site one is only adsorbed with high phenol concentrations. As for the phenol/RCG, the same effect of not presenting an equilibrium at this concentration indicates that the RCG can further adsorb phenol. The lack of a ladder-type profile also corroborates the entropic change, which presents a single peak, meaning that the adsorption of phenol is spontaneous and occurs in the same way for the two receptor sites.

**Figure 15.** Entropic change according to the phenol equilibrium, temperature, and adsorption system, RCA (**A**) and RCG (**B**).

The simulations for the internal energy according to the phenol equilibrium concentration and system temperature are shown in Figure 17. As expected, the internal energy presents similar behavior to the simulations obtained for the configurational entropy and Gibbs free energy. In other words, the presence of ladders in the phenol/RCA system, due to the energic differences between the receptor sites and the lack of equilibrium for the phenol/RCG system, indicates that more phenol molecules could be adsorbed. Overall, the internal energy tends to increase with temperature, corroborating the results obtained for the experimental isotherms.

**Figure 16.** Gibbs free energy changes according to the phenol concentration, temperature, and system, RCA (**A**) and RCG (**B**).

**Figure 17.** Internal energy changes according to the equilibrium concentration, temperature, and system, RCA (**A**) and RCG (**B**).

#### 3.10.5. Mechanism of Phenol Adsorption

The adsorption process of phenol on clay usually relies on several physicochemical forces occurring at the solid-liquid interface, such as the interaction between phenol molecules and clay functional groups


In this work, we try to deepen the study of the mechanism of the adsorption of phenol on clay. For that, structural and textural characterization of the samples was used to determine the nature of the interactions between the adsorbate and the adsorbent either by FTIR or XRD (see the supplement of this work).

To better understand the adsorption mechanism of clay materials, the infrared spectra of phenol and clay were examined. Figures S3 and S5 (Supplementary Materials) show the FTIR spectra of RCG and RCA both before and after phenol adsorption. The internal OH units of the kaolinite structure and the water of hydration of sodium cations are responsible for the characteristic RCG and RCA bands at 3625 and 3420 cm−1, respectively. [50]. With increasing phenol content, the intensity of the RCA and RCG bands (3400–3650 cm−1) increases. These findings imply that phenol penetrates the kaolinite interlayer and forms a hydrogen connection with the water molecules in the cationic hydration spheres. In all samples, the water OH groups' bands at 1638 and 3467 cm−<sup>1</sup> appear first. Some bands developed in the spectra of the sample after phenol adsorption as opposed to the new peaks not seen in the Agourai clay samples before and after adsorption. While the band at 1584 cm−<sup>1</sup> is caused by C-O vibrations, the band at 1479 cm−<sup>1</sup> is caused by the stretching of the aromatic C=C bond. The CH band for Ph(10−<sup>3</sup> M)/RCG is situated at 1450 cm−<sup>1</sup> in Figure 9. This alteration in the plane of the phenol CH group is brought on by bending vibrations. Additionally, vibrations below 1030 cm−<sup>1</sup> that correspond to the Si-O deformation and Si-O-Si stretching modes show band separation, indicating that they are involved in phenolic interactions [51]. Figures S2 and S6 (Supplementary Materials) displays the tilemaps for the RCG and CCG. The DRX analysis suggests that the interlobar space of the solid has been stripped as a result of the insertion of phenol into this space with a large dip angle, as seen by the removal of the quartz line at 20◦ in the spectrum in the RCG spectrum following phenol adsorption [45]. The distance from the quartz plane may only be extended if this adsorption mechanism is permitted; the average diameter of the phenol molecule is 5, depending on how it enters the interlayer gap. [15,52,53]. In addition, the maximum intensity in the RCG spectrum was reduced after phenol adsorption. In the clay samples collected in the Meknes region, the only thing missing was the lines in the raw clay attributed to calcite. In both diffractograms, there is no movement of lines or the appearance of new lines. Only one effect can be observed, and that is the reduction in peak intensity. After phenol adsorption, neither solid's spectrum displayed any new peaks, supporting the earlier finding of phenol's physical adsorption. According to the findings of XRD, FTIR, and SEM, surface adsorption rather than intercalation were the primary interactions between phenols and clay particles, which happened at the outer surface by electrostatic attraction.

#### **4. Conclusions**

The results of this laboratory-scale study demonstrate the utility of using RCA and RCG in the field of remediation of water bodies contaminated with organic pollutants. The adsorption kinetics of phenol on different adsorbents allowed the selection of Geulmima clay as the optimal adsorbent for phenol in an aqueous solution. The parameters under study, the pH value, and the temperature of the medium influence this kinetics. However, the structure and structural characterization of the solid before and after phenol adsorption indicated that the mechanism of the reaction was electrostatic and that hydrogen bonding played an important role in RCG adsorption of phenol, and kinetic modeling showed pseudo-second-order model dynamics. From the physical-statistical modeling, it was found that the phenol adsorption for both cases is well represented by the monolayer model with two types of energy sites, irrespective of the origin of the clay. However, the characteristics of each material direct reflect the steric parameters, as indicated by the model. The RCA has a receptor site with low density but with a high number of molecules and another receptor site that presents a more equilibrate behavior, being able to increase the density with increasing system temperature. As for the RCG, it was found that both receptor sites had similar energy and tend to work together in the increment of the adsorption capacity. Potential thermodynamic functions indicate that the adsorption of phenol is spontaneous and endothermic for all the studied systems, with all the thermodynamic proprieties tending to increase in absolute values with increasing temperature. Furthermore, the thermodynamic analysis indicated that the RCG can further adsorb phenol since the equilibrium is not reached for any variable. Characterization of the solid after phenol adsorption confirmed the physical nature of the process and the type of layer (van der Waals) and the role played by hydrogen bonds in the case of RCG. The latter result suggests that the main interaction between the phenolics and the outer precursor is through electrostatic attraction.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/w15101881/s1.

**Author Contributions:** Y.D., D.S.P.F., J.G., T.L., R.O. and Y.B.: Conceptualization, Methodology, Survey, Original Draft Writing, Editing Review; Y.D., D.S.P.F., J.G., T.L., R.O., Y.B., H.M., H.O., A.S. and B.M.: Methodology, Survey, Original draft writing, editing; Y.D., D.S.P.F., Y.B., R.O. and T.L.: Conceptualization, Methodology, Resources, Writing, Editing, Supervision, Acquiring Funding. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

**Acknowledgments:** The authors also thank everyone who helped prepare the manuscript.

**Conflicts of Interest:** We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

#### **References**


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## *Article* **In Situ Polyaniline Immobilized ZnO Nanorods for Efficient Adsorptive Detoxification of Cr (VI) from Aquatic System**

**Fahad A. Alharthi \*, Riyadh H. Alshammari and Imran Hasan \***

Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia; ralshammari@ksu.edu.sa

**\*** Correspondence: fharthi@ksu.edu.sa (F.A.A.); iabdulateef@ksu.edu.sa (I.H.)

**Abstract:** The elimination of toxic heavy metal ions from wastewater has been found to be of great importance in human as well marine animal wellbeing. Among various heavy metals, Cr (VI) has been found to be one of the highly toxic and carcinogenic heavy metals which are found to be dissolved in the water stream, the urgent treatment of which needs to be a priority. The present study demonstrates the fabrication of zinc oxide nanorods (ZnO NRs) and an immobilized polyaniline nanorod (ZnO@PAni NR) composite through an in situ free radical polymerization reactions. The material synthesis and purity were verified by X-ray diffractometer (XRD), Fourier transform infrared (FTIR), scanning electron microscope (SEM), energy dispersive spectroscope (EDS), and transmission electron microscope (TEM). Further, ZnO@PAni NRs were applied as an adsorbent for Cr (VI) in the aquatic system and exhibited a tremendous removal efficiency of 98.76%. The impact of operating parameters such as dose effect and pH on adsorption properties were studied. The uptake mechanism of Cr (VI) by ZnO@PAni was best explained by pseudo-second-order reaction, which suggested that the adsorption of Cr (VI) by the synthesized adsorbent material was processed by chemisorption, i.e., through formation of chemical bonds. The adsorption process proved viable and endothermic thermodynamically, and best supported by a Langmuir model, suggesting a monolayer formation of Cr (VI) on the surface of ZnO@PAni NRs.

**Keywords:** conducting polymers; wastewater treatment; Cr (VI) management; nanocomposites; zinc oxide

#### **1. Introduction**

The combined effects of extensive industrialization, population increase, agricultural activity, and other geological and environmental changes have led to an alarming decline in water quality [1–3]. Industries have become a significant contributor to water pollution by introducing harmful chemicals and heavy metals that have a substantial negative impact on both human and animal health, as well as causing widespread ecological harm [4–6]. Among various heavy metals, Cr (VI) has been recognized as a most harmful pollutant, causing various devastating effects such as the irritation and corrosion of human skin; further, it can lead to rust in the textile and metal finishing industries and can interfere with other processes including electroplating, dyeing, wood preservation, painting, fertilizing, and photography [7–11]. Usually in water streams, chromium exists in two different chemical states: Cr (III) and Cr (VI); Cr (VI) is genotoxic and carcinogenic to both humans and animals as compared to Cr (III), which is less toxic [12,13]. Therefore, based on its genotoxic and carcinogenic effects, it has been necessary to develop types of costeffective and efficient methods which can remove of Cr (VI) from industrial and municipal wastewater. Scientists and researchers have developed various strategical methods such as sedimentation, precipitation, coagulation, flotation, ion exchange, biological treatment, adsorption, and reverse osmosis to reduce environmental problems [14–17]. Among them, adsorption has been preferred as the most effective and efficient method because of its

**Citation:** Alharthi, F.A.; Alshammari, R.H.; Hasan, I. In Situ Polyaniline Immobilized ZnO Nanorods for Efficient Adsorptive Detoxification of Cr (VI) from Aquatic System. *Water* **2023**, *15*, 1949. https://doi.org/ 10.3390/w15101949

Academic Editor: Hai Nguyen Tran

Received: 30 March 2023 Revised: 9 May 2023 Accepted: 9 May 2023 Published: 21 May 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

operational simplicity and environmental friendliness for removing ultra-trace levels of Cr (VI) from wastewater [18–20]. Although adsorption has been proved to be very effective for heavy metal removal, the efficiency of the method depends upon the development of such types of adsorbent materials that can provide an ample number of surface-active sites to bind the heavy metal ions [21].

Recently, metal-oxide-based nanocomposites have been extensively utilized as adsorbent for the removal of heavy metals owing to their large specific surface area, high porosity, ample adsorption sites, and high removal efficiency [22–24]. Various metal oxide nanoparticles such as Fe2O3, Fe3O4, TiO2, SiO2, CuO, CeO2, and ZnO have been explored in the literature for Cr (VI) removal [1,25–30]. Among these, the ZnO nanoparticle has been recognized as one of the most propitious materials as an adsorbent for the removal of heavy metal ions because of its low cost, high surface area, and optimized electronic structure [25,31]. However, the degree of agglomeration limits these nanoparticles' efficiency, and to address this issue considerable effort has been made in the past to functionalize the surface of ZnO NPs with some electron-donating functional groups such as -OH, -COOH, -SH, -NH2, etc. [32].

Nowadays, conducting polymers with metal-oxide-based nanocomposite material have become a popular alternative for the sequestration of heavy metals from wastewater because they primarily offer more interfacial surface area for the adhesion of heavy metal ions, are simple to synthesize, and are cost-effective [33]. Polyaniline (PAni) is a highly conducting polymer which has excellent conducting properties and electronic features [34]. PAni has been recognized as one of the cost-effective conductive supports that improve the adsorptive properties of these hybrid nanocomposite materials towards heavy metals through the provision of electron-donating amine and imine groups on the surface of the material [35]. The functional groups and electronic properties in PAni generally involve electrostatic attractions and π–π interactions in the adsorption process which synergistically add on to the efficiency of ZnO NPs [36]. The addition of fillers such as natural clays, metal oxide nanoparticles, etc., can improve the stability of the polymer matrix [37].

Table 1 comprises some of the specific methods for synthesis of ZnO NPs using various modes and their advantages and disadvantages.


**Table 1.** Synthesis methods of ZnO nanoparticles.

In the present study, our research group has synthesized ZnO@PAni NRs using an in-situ synthesis approach. Furthermore, adsorption properties of the prepared ZnO@PAni NRs were investigated for the removal of Cr (VI) from an aqueous solution.

#### **2. Experimental Section**

#### *2.1. Chemicals*

Zinc nitrate hexahydrate (Zn (NO3)2.6H2O, 98% RG), sodium hydroxide pallets (97%, ACS grade), and hydrochloric acid (HCl, 37% ACS grade) were purchased from Sigma Aldrich (St. Luis, MO, USA). Aniline (monomer, 99.5%) and ammonium per sulphate (APS, (NH4)2S2O8, 98% RG) were supplied by Alpha Aesar (Somerville, MA, USA). All the chemicals and reagents were used as received without any further purification.

#### *2.2. Synthesis of Zinc Oxide Nanorods (ZnO NRs)*

The ZnO NRs were synthesized by the one-pot chemical coprecipitation method reported elsewhere, with some modification [46]. A 0.2 M solution of zinc acetate was prepared in ethanol and under magnetic stirring to attain homogeneity. A 15 mL solution of 0.1 M KOH was added dropwise until the pH of the reaction reached 9–10 and left on magnetic stirring for 12 h. After the stimulated time, the product was collected through centrifuge, washed with deionized water and absolute alcohol in order to remove any unreacted species, dehydrated in a hot-air oven under 90 ◦C temperature for 3 h, and calcined at 500 ◦C for another 3 h.

#### *2.3. Synthesis of ZnO Immobilized PAni NRs*

A method of oxidative free radical polymerization was utilized for the synthesis of the material reported elsewhere [47]. In a conical flask, a 2% (*w*/*v*) colloidal solution of ZnO was taken and sonicated for 30 min at 25 ◦C in an HCl solvent system. After sonication, a solution of 10% (*v*/*v*) aniline monomer in 0.1 M HCl solution was added to the dispersed colloidal system and magnetically mixed thoroughly to attain a homogeneous condition. After achieving homogeneity, a solid powder of ammonium persulfate of approximately 12.5 g was added to the colloidal mixture prepared above and the reaction was left on ice bath for 12 h. After the stimulated time, the reaction was stopped by adding an excess amount of HCl, and a green-colored precipitate was obtained, which was filtered and washed with deionized water several times to remove unreacted species. The material was dehydrated in a hot-air oven at 80 ◦C for 4 h.

#### *2.4. Apparatus Used for Characterization*

For recording XRD patterns in the current investigation, a powder X-ray diffractometer (XRD) from Rigaku, Tokyo, Japan, was used. Scanning electron microscopy (SEM) photographs of the produced materials were taken using a Zeiss microscope (Jena, Germany). On an Oxford EDX ( MA, USA) apparatus coupled to an SEM, research using the energy dispersive X-ray spectroscopy (EDX) technique was carried out. On a Tecnai G2, F30 apparatus, pictures of the acquired samples under a transmission electron microscope (TEM) were taken. TGA analysis was carried out on Perkin Elmer model STA 6000 under an N2 environment. We used a BRUKER spectrometer to record the Fourier transform infrared (FTIR) spectra of the prepared samples. BET analysis was performed on a BET Surface Area Analyzer quanta chrome, Autosorb iQ2. The remaining concentration of Cr (VI) ions in the solution after adsorption was determined by using an atomic absorption spectrometer (AAS) GBC-908AA.

#### *2.5. Adsorption of Cr (VI) Experiments*

The adsorption experiments were performed according to the batch method. An amount of 0.02 g of ZnO@PAni NRs was dispersed in 20 mL of 50 mg/L Cr (VI) solution and was placed in sa 50 mL Erlenmeyer flask and shaken into a thermostat water bath at 100 rpm at variable temperature values such as 298 K, 308 K, and 318 K. The effects of various reaction variables such as contact time (50–200 min), pH of the medium (2–7), and adsorbent dose (12–28 mg) were optimized. The adsorption capacity of the synthesized material was evaluated by using Equation (1):

$$\mathbf{q}\_{\mathbf{e}} = \left(\frac{\mathbf{C}\_0 - \mathbf{C}\_{\mathbf{e}}}{\mathbf{C}\_0}\right) \times 100 \tag{1}$$

where q<sup>e</sup> is the amount of Cr (VI) adsorbed per gram of the adsorbent at equilibrium, C<sup>0</sup> and C<sup>e</sup> are the initial and final concentration of Cr (VI) ions, respectively, V is the volume of the metal ion solution taken, and W is the mass of the adsorbent taken (g).

The pH of the solution can easily influence the migration of charge on the surfaces of the adsorbent. Therefore, adsorption experiments were performed by varying the pH of the 50 mg/L Cr (VI) solution 15 mg adsorbent dose. The amount of adsorbent has a significant impact on how well metal ions in the solution are able to be absorbed. The effect of various adsorbent doses was optimized for improved adsorption capacity. A variation of adsorbent dose from 12 to 28 mg was used in 20 mL of 50 mg/L aqueous solution of Cr (VI) to investigate the impact of the adsorbent dose on the removal of Cr (VI).

#### *2.6. Effects of Variable Nanorod Size, Initial Cr (VI) Concentration, and Individual Constituents on Adsorption*

The effect of varying nanorod size on the adsorption capacity of ZnO@PAni towards Cr (VI) was observed by synthesizing the material with nanorods of various sizes such as 10.5, 19, 32.6, and 52.15 nm. Experiments were performed by taking 20 mL of 50 ppm Cr (VI) solution with 13 mg of the adsorbent dose. The adsorption efficiency of the synthesized material ZnO@PAni was observed towards the variable Cr (VI) concentration from 10 mg/L to 100 mg/L at an optimized reaction condition. Simultaneously the effects of individual constituents, viz. ZnO NRs, PAni, and ZnO@PAni NRs, on Cr (VI) adsorption were also studied at optimized reaction conditions.

#### *2.7. Adsorption Isotherms*

The equilibrium attained after the uptake of the metal ion by the adsorbent surface was studied by adsorption isotherms. The present work involves the nonlinear regression analysis of two isotherm models, namely, the Langmuir [48] and Freundlich [49] models, to study the adsorption properties of ZnO@PAni towards Cr (VI).

#### 2.7.1. Langmuir Isotherm

According to the Langmuir model, a reversible chemical equilibrium happens between the ZnO@PAni surface and Cr (VI) ions with a validity of monolayer formation on a surface with a limited number of identical sites, which is nonlinearly represented by Equation (2).

$$\mathbf{q}\_{\mathbf{e}} = \frac{\mathbf{q}\_{\mathrm{m}} \mathbf{K}\_{\mathrm{L}} \mathbf{C}\_{\mathrm{e}}}{1 + \mathbf{K}\_{\mathrm{L}} \mathbf{C}\_{\mathrm{e}}} \tag{2}$$

#### 2.7.2. Freundlich Isotherm

Equation (3) represents the empirical linear equation pertaining to the Freundlich model, which is based on the idea of a heterogenous surface and reversible multilayer adsorption. The model is nonlinearly represented by Equation (3):

$$\mathbf{q}\_{\mathbf{e}} = \mathbf{K}\_{\mathbf{F}} \mathbf{C}\_{\mathbf{e}}^{1/\mathbf{n}} \tag{3}$$

Herein, n is the degree of favorability of adsorption reaction, e.g., if n > 1, then adsorption is favorable, while if n < 1, then adsorption is non-favorable. K<sup>F</sup> (mg/g) is the Freundlich adsorption capacity.

#### *2.8. Adsorption Kinetics*

#### 2.8.1. Pseudo-First Order

The adsorption rate is directly proportional to the difference between the equilibrium adsorption capacity and the adsorption capacity at any given time t, according to the pseudo-first-order kinetics model, which is based on the premise that adsorption is regulated by diffusion steps [50],

$$\mathbf{q}\_{\mathbf{t}} = \mathbf{q}\_{\mathbf{e}} \left( \mathbf{1} - \mathbf{e}^{-\mathbf{k}\_{\mathbf{l}} \mathbf{t}} \right) \tag{4}$$

Herein, q<sup>e</sup> = amount of Cr (VI) adsorbed on ZnO@PAni (mg/g) at equilibrium, and q<sup>t</sup> = amount of Cr (VI) adsorbed (mg/g) at time t.

#### 2.8.2. Pseudo-Second Order

The following equation provides the linear equation for pseudo-second-order kinetics [51],

$$\mathbf{q}\_{\rm t} = \frac{\mathbf{k}\_2 \mathbf{q}\_{\rm e}^2 \mathbf{t}}{1 + \mathbf{k}\_2 \mathbf{q}\_{\rm e} \mathbf{t}} \tag{5}$$

where k<sup>2</sup> = pseudo-second-order rate constant, q<sup>e</sup> = amount of Cr (VI) adsorbed on ZnO@PAni (mg/g) at equilibrium, and q<sup>t</sup> = amount of adsorbed (mg/g) of Cr (VI) at time t.

#### 2.8.3. Elovich Model

The Elovich equation may be used to understand the sorption kinetics as follows if the process is a chemisorption on highly heterogeneous sorbents [52]:

$$\mathbf{q}\_{\mathbf{t}} = \frac{1}{\beta} \ln(\alpha \beta \mathbf{t} + 1) \tag{6}$$

Herein, α = initial adsorption rate (mg/g·min), β = desorption constant (g/mg), and q<sup>t</sup> = adsorption capacity at any time t (mg/g).

#### 2.8.4. Intraparticle Diffusion Model

In a batch reactor system, pore and intraparticle diffusion are frequently rate-limiting when an adsorbent is translocated from the solution into the solid phase of absorbents [53]. Intraparticle diffusion was investigated using the Weber and Morris equation, as given below:

$$\mathbf{q}\_{\mathbf{t}} = \mathbf{K}\_{\text{int}} \times \mathbf{t}^{0.5} + \mathbf{C} \tag{7}$$

In Equation (12), q<sup>t</sup> = amount of Cr (VI) adsorbed (mg/g) at time t, Kint = intraparticle diffusion constant (mg/g·min0.5), and t = time.

#### *2.9. Adsorption Thermodynamics*

The Gibbs equation and the van't Hoff equation were used to compute thermodynamic parameters such as the Gibbs free energy change (G◦ ), enthalpy change (H◦ ), and entropy change (S◦ ) in order to support our assertion that the adsorption process is endothermic. The equilibrium constant using the Langmuir constant (KL) is given by Equation (8) [54]

$$\mathbf{K}\_{\rm{eq}} = \frac{\mathbf{K}\_{\rm{L}} \times \mathbf{M}\_{\rm{A}}}{\gamma\_{\rm{e}}} \tag{8}$$

where K<sup>L</sup> is the Langmuir constant (L/mg), M<sup>A</sup> is the molar mass of Cr (VI) in mg, and γ<sup>e</sup> is the activity of adsorbate, i.e., Cr (VI). For non-ionic species or dilute solutions, γ<sup>e</sup> = 1, so Equation (8) reduces to Equation (9) [55];

$$\mathbf{K\_{eq}} = \mathbf{K\_L} \times \mathbf{M\_A} \tag{9}$$

The equilibrium constant is related to Gibbs free energy by Equation (10);

$$
\Delta \mathbf{G} = -\mathbf{R} \mathbf{T} \ln \mathbf{K\_{eq}} \tag{10}
$$

since

$$
\Delta \mathbf{G} = \Delta \mathbf{H} - \mathbf{T} \Delta \mathbf{S} \tag{11}
$$

Using Equations (10) and (11), we obtained the following:

$$
\ln \text{K}\_{\text{eq}} = -\frac{\Delta \text{H}}{\text{RT}} + \frac{\Delta \text{S}}{\text{R}} \tag{12}
$$

R = gas constant, Keq = equilibrium constant, and T = temperature of solution. The values of ∆H and ∆S are determined from the slope and intercept of a plot of ln Keq as a function of 1/T. The following Equation (11) can be used to compute the free energy change (∆G) of the adsorption reaction.

#### *2.10. Determination of Point of Zero Charge and Zeta Potential*

The point of zero charge (pHpzc) signifies the state of a material when the net electrical charge on its surface becomes zero; at this stage, adsorption takes place through an ion exchange process [56]. To determine the pHpzc, experiments were conducted in a batch mode by taking 20 mL of 0.1 M KCl solution with 50 mg of adsorbent with 10 replicates. The pH of all the 10 samples was adjusted from 1 to 10 using 0.1 M HCl and NaOH solutions. After 24 h, the supernatants were collected using centrifuge and employed to measure the ζ potential of the solution. A curv6e of ζ potential vs. pH was plotted and is given in Figure S1. The ζ potential was positive till pH 4 and negative from pH 5 to 10. The value of pHpzc was found to be 4.2, which signifies the positive surface of the adsorbent below pH 4.2 and negative surface above pH 4.2.

#### **3. Results and Discussion**

#### *3.1. Characterization of ZnO@PAni*

Surface morphological properties of the ZnO, PAni, and ZnO@PAni were examined by SEM analysis, and elemental composition was confirmed by EDX. Figure 1a demonstrates the recorded SEM image of PAni which shows flakelike surface structures while the ZnO nanoparticles exhibited a nanowire-type-shaped surface with agglomerations, as presented in Figure 1b. Thus, the SEM investigations suggested that ZnO and PAni possess different surface morphologies as described above. Therefore, it was an interesting thought to utilize the surface properties of both ZnO and PAni to synthesize a composite ZnO@PAni with improved adsorption efficiency. The SEM results of ZnO@PAni indicated that ZnO nanowires are strongly attached on the PAni flakes (Figure 1c). The elemental composition of the prepared ZnO@PAni NRs has been examined by EDX studies, and Figure 1d exhibited the presence of C, N, Zn, and O elements, which revealed the formation of ZnO@PAni NRs with good phase purity. Figure 1e represents the SEM image of ZnO@PAni NRs after the adsorption of Cr (VI), which exhibits a surface covered with small dots on ZnO NRs and PAni flakes, which is due to adsorption of water molecules alongside Cr (VI) ions. Figure 1f represents the EDX spectra of Cr-adsorbed ZnO@PAni NRs, which represent the presence of elements such as C, O, Zn, and Cr. The absence of an N atom from the EDX spectra suggests the involvement of amine and imine groups in the binding of Cr (VI) from the aqueous solution.

alongside Cr (VI) ions. Figure 1f represents the EDX spectra of Cr-adsorbed ZnO@PAni NRs, which represent the presence of elements such as C, O, Zn, and Cr. The absence of an N atom from the EDX spectra suggests the involvement of amine and imine groups in

the binding of Cr (VI) from the aqueous solution.

**Figure 1.** SEM image of (**a**) PAni, (**b**) ZnO nanowires, and(**c**) ZnO@PAni; (**d**) EDX spectra of ZnO@PAni; (**e**) SEM image of ZnO@PAni after Cr (VI) adsorption; and (**f**) EDX spectra of ZnO@PAni-Cr (VI) adsorbed. **Figure 1.** SEM image of (**a**) PAni, (**b**) ZnO nanowires, and(**c**) ZnO@PAni; (**d**) EDX spectra of ZnO@PAni; (**e**) SEM image of ZnO@PAni after Cr (VI) adsorption; and (**f**) EDX spectra of ZnO@PAni-Cr (VI) adsorbed.

The morphological properties of the prepared ZnO@PAni were further authenticated by recording a TEM image. The TEM image of the synthesized ZnO@PAni NRs showed that ZnO nanowires are immobilized with PAni flakes (Figure 2a). Therefore, this confirms the formation of ZnO@PAni NRs using an in situ synthetic method. The Gaussian distribution profiles for average particle size of ZnO@PAni NRs are presented in Figure 2b, which represents the average particle size as 22 nm. The morphological properties of the prepared ZnO@PAni were further authenticated by recording a TEM image. The TEM image of the synthesized ZnO@PAni NRs showed that ZnO nanowires are immobilized with PAni flakes (Figure 2a). Therefore, this confirms the formation of ZnO@PAni NRs using an in situ synthetic method. The Gaussian distribution profiles for average particle size of ZnO@PAni NRs are presented in Figure 2b, which represents the average particle size as 22 nm.

Fourier transform infrared spectroscopy (FTIR) was further used to analyze the type of functional groups formed during processing of the nanocomposite material involving ZnO, PAni, and ZnO@PAni composite and the results are given in Figure 3.

FTIR spectra of ZnO exhibited the presence of various bands at 3443, 1629, 1022, and 443 cm–1. The peak at 3443, 1629 cm–1 (stretching and bending vibrational modes of hydroxyl compounds (–OH groups)), and the characteristic peaks at 1022 and 443 cm−<sup>1</sup> of Zn-O bonds authenticates the formation of ZnO nanorods [57,58]. The FTIR spectrum of PAni exhibits an absorption band at ~829 cm−<sup>1</sup> (C–H bonding out of plane bending in the benzenoid ring), 515 cm–1 (C–N–C bonding mode of aromatic ring), 698 cm–1 (C–C and C–H bonding mode of aromatic ring), 1036, 1296, and 1499 cm–1 can be attributed to the C–N stretching of the benzenoid ring, and 1576 cm–1 may be due to the C–N stretching of the quinoid ring [59]. The FTIR spectrum of ZnO@PAni showed similar characteristics to the band of PAni. However, slight displacement in the peak was observed, which may be

due to the presence of ZnO on the surface of PAni. The band at 3355 cm–1 is due to the presence of hydrogen bonding formed between N-H bonds of PAni and oxygen of ZnO NRs [60]. *Water* **2023**, *15*, x FOR PEER REVIEW 8 of 21 10**(b)**

8

**Figure 2.** (**a**) TEM image of ZnO@PAni NRs, and (**b**) corresponding Gaussian distribution profiles for average particle size. **Figure 2.** (**a**) TEM image of ZnO@PAni NRs, and (**b**) corresponding Gaussian distribution profiles for average particle size. Fourier transform infrared spectroscopy (FTIR) was further used to analyze the type of functional groups formed during processing of the nanocomposite material involving ZnO, PAni, and ZnO@PAni composite and the results are given in Figure 3.

4000 3600 3200 2800 2400 2000 1600 1200 800 400 443 1629 1022 **Figure 3.** FTIR spectra of ZnO (black line) PAni (red line) and ZnO@PAni NRs (blue line) **Figure 3.** FTIR spectra of ZnO (black line) PAni (red line) and ZnO@PAni NRs (blue line).

**Figure 3.** FTIR spectra of ZnO (black line) PAni (red line) and ZnO@PAni NRs (blue line) FTIR spectra of ZnO exhibited the presence of various bands at 3443, 1629, 1022, and 443 cm–1 . The peak at 3443, 1629 cm–1 (stretching and bending vibrational modes of hydroxyl compounds (–OH groups)), and the characteristic peaks at 1022 and 443 cm−1 of Zn-O bonds authenticates the formation of ZnO nanorods [57,58]. The FTIR spectrum of PAni exhibits an absorption band at ~829 cm−1 (C–H bonding out of plane bending in the Wavenumber (cm-<sup>1</sup> ) FTIR spectra of ZnO exhibited the presence of various bands at 3443, 1629, 1022, and 443 cm–1 . The peak at 3443, 1629 cm–1 (stretching and bending vibrational modes of hydroxyl compounds (–OH groups)), and the characteristic peaks at 1022 and 443 cm−1 of Zn-O bonds authenticates the formation of ZnO nanorods [57,58]. The FTIR spectrum of PAni exhibits an absorption band at ~829 cm−1 (C–H bonding out of plane bending in the benzenoid ring), 515 cm–1 (C–N–C bonding mode of aromatic ring), 698 cm–1 (C–C and C– H bonding mode of aromatic ring), 1036, 1296, and 1499 cm–1 can be attributed to the C– N stretching of the benzenoid ring, and 1576 cm–1 may be due to the C–N stretching of the quinoid ring [59]. The FTIR spectrum of ZnO@PAni showed similar characteristics to the Furthermore, recorded XRD data of the ZnO, PAni, and ZnO/PAni are presented in Figure 4. The characteristic peaks of ZnO were observed in the recorded XRD pattern of ZnO, and presence of (011), (100), (002), (101), (020), (102), (302), (110), (103), and (112) diffraction planes confirmed the formation of ZnO corresponding to JCPDS No. 36–1451. The strong diffraction peaks indicated the good crystalline nature of the prepared ZnO. The XRD of PAni has been displayed in Figure 4, and a broad diffraction peak appeared at 25.5◦ . This is the characteristic peak of PAni and suggests the presence of the (200) diffraction plane in the prepared PAni. The XRD results for ZnO@PAni demonstrated the presence of the (200) diffraction plane of PAni and authenticated the formation of ZnO@PAni [47,61].

H bonding mode of aromatic ring), 1036, 1296, and 1499 cm–1 can be attributed to the C–

(C–N–C bonding mode of aromatic ring), 698 cm–1

(C–C and C–

benzenoid ring), 515 cm–1

However, crystallinity of the ZnO@PAni was reduced due to the amorphous nature of PAni (Figure 4). presence of the (200) diffraction plane of PAni and authenticated the formation of ZnO@PAni [47,61]. However, crystallinity of the ZnO@PAni was reduced due to the amorphous nature of PAni (Figure 4).

band of PAni. However, slight displacement in the peak was observed, which may be due

of hydrogen bonding formed between N-H bonds of PAni and oxygen of ZnO NRs [60]. Furthermore, recorded XRD data of the ZnO, PAni, and ZnO/PAni are presented in Figure 4. The characteristic peaks of ZnO were observed in the recorded XRD pattern of ZnO, and presence of (011), (100), (002), (101), (020), (102), (302), (110), (103), and (112) diffraction planes confirmed the formation of ZnO corresponding to JCPDS No. 36–1451. The strong diffraction peaks indicated the good crystalline nature of the prepared ZnO. The XRD of PAni has been displayed in Figure 4, and a broad diffraction peak appeared at 25.5°. This is the characteristic peak of PAni and suggests the presence of the (200) diffraction plane in the prepared PAni. The XRD results for ZnO@PAni demonstrated the

is due to the presence

*Water* **2023**, *15*, x FOR PEER REVIEW 9 of 21

to the presence of ZnO on the surface of PAni. The band at 3355 cm–1

**Figure 4.** XRD spectra of ZnO (black line) PAni (red line) and ZnO@PAni NRs (blue line) **Figure 4.** XRD spectra of ZnO (black line) PAni (red line) and ZnO@PAni NRs (blue line).

The thermal stability of ZnO@PAni and PAni were also investigated, and the obtained results are shown in Figure 5. The weight loss for PAni around 124 °C may be attributed to the evaporation of water. The weight loss for PAni at ~700–725 °C was found to be 57.46%. In the case of ZnO@PAni, the weight loss after 700 °C was found to be 44.69%. This shows that weight loss in ZnO@PAni is less compared to pristine PAni. Thus, the introduction of stable ZnO enhances the thermal stability of ZnO@PAni (Figure 5) [62]. The specific surface area of adsorbent plays a vital role, and it is necessary to study the surface area of ZnO@PAni. Brunauer–Emmett–Teller (BET) investigations were carried out to examine the specific surface area of the prepared ZnO@PAni. The nitrogen adsorption–desorption isotherm of ZnO@PAni has been presented in Figure 6a. The BET studies showed that ZnO@PAni has a high surface area of 113.5 m<sup>2</sup> /g. The pore width distribution curve of ZnO@PAni is shown in Figure 6b. The average pore width of the ZnO@PAni was found to be 7 nm. The thermal stability of ZnO@PAni and PAni were also investigated, and the obtained results are shown in Figure 5. The weight loss for PAni around 124 ◦C may be attributed to the evaporation of water. The weight loss for PAni at ~700–725 ◦C was found to be 57.46%. In the case of ZnO@PAni, the weight loss after 700 ◦C was found to be 44.69%. This shows that weight loss in ZnO@PAni is less compared to pristine PAni. Thus, the introduction of stable ZnO enhances the thermal stability of ZnO@PAni (Figure 5) [62]. The specific surface area of adsorbent plays a vital role, and it is necessary to study the surface area of ZnO@PAni. Brunauer–Emmett–Teller (BET) investigations were carried out to examine the specific surface area of the prepared ZnO@PAni. The nitrogen adsorption–desorption isotherm of ZnO@PAni has been presented in Figure 6a. The BET studies showed that ZnO@PAni has a high surface area of 113.5 m2/g. The pore width distribution curve of ZnO@PAni is shown in Figure 6b. The average pore width of the ZnO@PAni was found to be 7 nm. *Water* **2023**, *15*, x FOR PEER REVIEW 10 of 21

**Figure 5.** Thermogravimetric (TGA) profile for PAni (black line) and ZnO@PAni (red line). **Figure 5.** Thermogravimetric (TGA) profile for PAni (black line) and ZnO@PAni (red line).

**Figure 6.** (**a**) N<sup>2</sup> adsorption–desorption curve, and (**b**) corresponding BJH pore size distribution

The adsorption properties of synthesized ZnO@PAni NRs were explored towards Cr (VI) from an aqueous system, the effect of various reaction variables such as pH, adsorbent dose, and contact time were observed, and the results are given in Figure 7a–c. Figure 7a shows the 2D contour plot for the simultaneous interaction of contact time and pH on the adsorption capacity of ZnO@PAni NRs. According to the observations, ZnO@PAni exhibited maximum adsorption capacity for Cr (VI) at pH 2, which continues to decrease with

2− are more prom-

isprominent in an acidic medium, which are the key

(**a**) (**b**)

the further increase in pH value. Different forms of Cr (VI) such as CrO<sup>4</sup>

−

curve for ZnO@PAni NRs.

0.0 0.2 0.4 0.6 0.8 1.0

Relative pressure (P/P<sup>0</sup>

)

Adsorption-Desorption Curve

Volume Adsorbed (CC/g)

*3.2. Adsorption Studies*

inent in neutral pH, whereas HCrO<sup>4</sup>

**Figure 6.** (**a**) N<sup>2</sup> adsorption–desorption curve, and (**b**) corresponding BJH pore size distribution curve for ZnO@PAni NRs. **Figure 6.** (**a**) N<sup>2</sup> adsorption–desorption curve, and (**b**) corresponding BJH pore size distribution curve for ZnO@PAni NRs.

100 200 300 400 500 600 700 800

PAni ZnO@PAni

55.31%

42.54%

Temperature (°C)

**Figure 5.** Thermogravimetric (TGA) profile for PAni (black line) and ZnO@PAni (red line).

#### *3.2. Adsorption Studies 3.2. Adsorption Studies*

40

50

60

70

Weight (%)

80

90

100

The adsorption properties of synthesized ZnO@PAni NRs were explored towards Cr (VI) from an aqueous system, the effect of various reaction variables such as pH, adsorbent dose, and contact time were observed, and the results are given in Figure 7a–c. Figure 7a shows the 2D contour plot for the simultaneous interaction of contact time and pH on the adsorption capacity of ZnO@PAni NRs. According to the observations, ZnO@PAni exhibited maximum adsorption capacity for Cr (VI) at pH 2, which continues to decrease with the further increase in pH value. Different forms of Cr (VI) such as CrO<sup>4</sup> 2− are more prominent in neutral pH, whereas HCrO<sup>4</sup> − isprominent in an acidic medium, which are the key The adsorption properties of synthesized ZnO@PAni NRs were explored towards Cr (VI) from an aqueous system, the effect of various reaction variables such as pH, adsorbent dose, and contact time were observed, and the results are given in Figure 7a–c. Figure 7a shows the 2D contour plot for the simultaneous interaction of contact time and pH on the adsorption capacity of ZnO@PAni NRs. According to the observations, ZnO@PAni exhibited maximum adsorption capacity for Cr (VI) at pH 2, which continues to decrease with the further increase in pH value. Different forms of Cr (VI) such as CrO<sup>4</sup> <sup>2</sup><sup>−</sup> are more prominent in neutral pH, whereas HCrO<sup>4</sup> − is prominent in an acidic medium, which are the key factors for the adsorption of Cr (VI) on the ZnO@PAni surface [63]. Regarding a pH less than 4 (pHpzc = 4.2, Figure 7d), the adsorbent surface becomes positively charged due to protonation in the presence of an excess of H<sup>+</sup> ions in the solution, and at lower pH, the HCrO<sup>4</sup> − form of Cr (VI) ions predominate. Higher removal efficiency resulted from the strong electrostatic interaction between the positively charged adsorbent surface and the negatively charged HCrO<sup>4</sup> ions. With increasing pH, deprotonation of the surface of ZnO@PAni can occur due to decreasing H<sup>+</sup> ions [64].

Therefore, at higher pH values, there is less interaction between Cr (VI) ions and adsorbent surface, which leads to lesser adsorption capacity (Figure 7a). The simultaneous effect of contact time (min) on the adsorption of Cr (VI) was also observed in a time span of 50–200 min. Figure 7a shows that >35 mg/g adsorption capacity was observed at 120 min of contact time, and after that a negligible variation was observed with further increase in time, suggesting the occupation of all the surface-active sites or saturation of the adsorbent surface by Cr (VI) ions. Therefore, 120 min was chosen as the optimized time for further adsorption experiments.

The effect of various adsorbent doses was optimized for improved adsorption capacity. The results presented in Figure 7b suggest that the adsorption capacity of ZnO@PAni towards Cr (VI) decreases with an increase in adsorbent dose. The optimized amount of 13 mg is a more suitable adsorbent for Cr (VI) removal, which shows maximum adsorbent capacity. Initially with a lower amount of adsorbent dose, the higher number of surfaceactive sites are available to bind with Cr (VI) and as a result a high value of adsorbent capacity is achieved. At a higher adsorbent dose, due to the agglomeration of nanoparticles, a smaller number of surface-active sites are available and as a result a lower value of adsorbent capacity occurs [65]. Furthermore, we have also investigated the adsorption capacity by varying the pH and adsorbent dose, and the observations indicated that the

highest adsorption capacity of more than 50 mg/g for 13 mg ZnO@PAni in an aqueous solution of pH 2 (Figure 7c) was achieved. Therefore, it was concluded that ZnO@PAni has the potential to adsorb the Cr (VI) effectively. To further confirm the adsorption of Cr (VI) by ZnO@PAni, we have recorded a SEM image of the ZnO@PAni surface after Cr (VI) adsorption. Figure 1e shows the recorded SEM image of ZnO@PAni with Cr (VI) adsorption. The Cr (VI) particles are seen on the surface of ZnO@PAni. The presence of Cr (VI) on ZnO@PAni was also further authenticated by EDX. Figure 1f shows the obtained EDX data of Cr (VI)-adsorbed ZnO@PAni. The EDX data reveal the presence of Cr, C, Zn, and O elements. Thus, it is confirmed that Cr (VI) particles are present on the ZnO@PAni surface. This confirms the adsorption properties of ZnO@PAni nanocomposite. factors for the adsorption of Cr (VI) on the ZnO@PAni surface [63]. Regarding a pH less than 4 (pHpzc = 4.2, Figure 7d), the adsorbent surface becomes positively charged due to protonation in the presence of an excess of H+ ions in the solution, and at lower pH, the HCrO4− form of Cr (VI) ions predominate. Higher removal efficiency resulted from the strong electrostatic interaction between the positively charged adsorbent surface and the negatively charged HCrO4 ions. With increasing pH, deprotonation of the surface of ZnO@PAni can occur due to decreasing H+ ions [64].

*Water* **2023**, *15*, x FOR PEER REVIEW 11 of 21

**Figure 7.** The 2D contour plots for optimization of process variable: (**a**) pH vs. contact time, (**b**) adsorbent dose vs. contact time, and (**c**) pH vs. adsorbent dose for removal of (50 mg/L) Cr (VI) by ZnO@PAni; (**d**) pH vs. zeta potential curve for the determination of the point of zero charge (pHpzc). **Figure 7.** The 2D contour plots for optimization of process variable: (**a**) pH vs. contact time, (**b**) adsorbent dose vs. contact time, and (**c**) pH vs. adsorbent dose for removal of (50 mg/L) Cr (VI) by ZnO@PAni; (**d**) pH vs. zeta potential curve for the determination of the point of zero charge (pHpzc).

#### Therefore, at higher pH values, there is less interaction between Cr (VI) ions and adsorbent surface, which leads to lesser adsorption capacity (Figure 7a). The simultaneous *3.3. Effect of Varying Nanorod Size, Initial Cr (VI) Concentration, and Individual Adsorbent Material*

effect of contact time (min) on the adsorption of Cr (VI) was also observed in a time span of 50–200 min. Figure 7a shows that >35 mg/g adsorption capacity was observed at 120 min of contact time, and after that a negligible variation was observed with further increase in time, suggesting the occupation of all the surface-active sites or saturation of the adsorbent surface by Cr (VI) ions. Therefore, 120 min was chosen as the optimized time for further adsorption experiments. The effect of various adsorbent doses was optimized for improved adsorption capac-The effect of varying nanorod size on the adsorption capacity of ZnO@PAni towards Cr (VI) was observed, and the obtained results are summarized in Figure 8a. The observations demonstrated that with the increase in nanorod size from 10.5 to 20 nm, adsorption capacity of ZnO@PAni increased from 25.56 to 37.93 mg/g, while beyond 20 nm, a gradual decrease in adsorption capacity was observed, which might be due to the lower surface area possessed by larger-size nanorods [66]. Initially, at 10.5 nm nanorod size, there may be a possibility of stacking of nanorods due to small size and hence a lower adsorption

ity. The results presented in Figure 7b suggest that the adsorption capacity of ZnO@PAni towards Cr (VI) decreases with an increase in adsorbent dose. The optimized amount of

active sites are available to bind with Cr (VI) and as a result a high value of adsorbent capacity is achieved. At a higher adsorbent dose, due to the agglomeration of nanoparticles, a smaller number of surface-active sites are available and as a result a lower value of

*Material*

(VI) [67,68].

capacity was observed. As the particle size increased to 19 nm, the possibility of agglomeration decreased, and as a result, nanorods were distributed throughout the aqueous media and thus a high adsorption capacity was achieved. At a higher particle size, due to low surface area, a smaller number of surface-active sites were available and hence a decrease in adsorption capacity was constantly observed. The adsorption efficiency of the synthesized material ZnO@PAni was observed towards the variable Cr (VI) concentration and the obtained results are summarized in Figure 8b. The observations and results suggested that with gradual increase in Cr (VI) concentration from 10 mg/L to 100 mg/L, the adsorption capacity of ZnO@PAni also increases. This can be explained as the adsorbent consists of plenty of surface-active sites that can easily accommodate Cr (VI) ions, even at a 100 mg/L concentration. Figure 8c represents the adsorption capacity bars with respect to the synthesized material (ZnO@PAni) and its constituent materials, including ZnO and PAni. It was observed that the composite formed by assembling ZnO and PAni results in higher adsorption capacity as compared to its individual constituents. This is due to the synergistic effect provided by -OH groups on the surface of ZnO and -NH<sup>2</sup> groups on PAni that provide plenty of charge density to bind the positively charged Cr (VI) [67,68]. size, due to low surface area, a smaller number of surface-active sites were available and hence a decrease in adsorption capacity was constantly observed. The adsorption efficiency of the synthesized material ZnO@PAni was observed towards the variable Cr (VI) concentration and the obtained results are summarized in Figure 8b. The observations and results suggested that with gradual increase in Cr (VI) concentration from 10 mg/L to 100 mg/L, the adsorption capacity of ZnO@PAni also increases. This can be explained as the adsorbent consists of plenty of surface-active sites that can easily accommodate Cr (VI) ions, even at a 100 mg/L concentration. Figure 8c represents the adsorption capacity bars with respect to the synthesized material (ZnO@PAni) and its constituent materials, including ZnO and PAni. It was observed that the composite formed by assembling ZnO and PAni results in higher adsorption capacity as compared to its individual constituents. This is due to the synergistic effect provided by -OH groups on the surface of ZnO and -NH<sup>2</sup> groups on PAni that provide plenty of charge density to bind the positively charged Cr

**Figure 8.** Variation of adsorption capacity of ZnO@PAni with respect to (**a**) particle size, (**b**) initial Cr (VI) concentration, and (**c**) the synthesized material and its constituents*.* **Figure 8.** Variation of adsorption capacity of ZnO@PAni with respect to (**a**) particle size, (**b**) initial Cr (VI) concentration, and (**c**) the synthesized material and its constituents.

#### *3.4. Adsorption Isotherm Studies*

of ZnO@PAni NRs towards Cr (VI) [69,70].

at 298 K, 308 K, and 318 K.

Langmuir

Freundlich

*Water* **2023**, *15*, x FOR PEER REVIEW 12 of 21

capacity by varying the pH and adsorbent dose, and the observations indicated that the highest adsorption capacity of more than 50 mg/g for 13 mg ZnO@PAni in an aqueous solution of pH 2 (Figure 7c) was achieved. Therefore, it was concluded that ZnO@PAni has the potential to adsorb the Cr (VI) effectively. To further confirm the adsorption of Cr (VI) by ZnO@PAni, we have recorded a SEM image of the ZnO@PAni surface after Cr (VI) adsorption. Figure 1e shows the recorded SEM image of ZnO@PAni with Cr (VI) adsorption. The Cr (VI) particles are seen on the surface of ZnO@PAni. The presence of Cr (VI) on ZnO@PAni was also further authenticated by EDX. Figure 1f shows the obtained EDX data of Cr (VI)-adsorbed ZnO@PAni. The EDX data reveal the presence of Cr, C, Zn, and O elements. Thus, it is confirmed that Cr (VI) particles are present on the ZnO@PAni sur-

face. This confirms the adsorption properties of ZnO@PAni nanocomposite.

*3.3. Effect of Varying Nanorod Size, Initial Cr (VI) Concentration, and Individual Adsorbent* 

The effect of varying nanorod size on the adsorption capacity of ZnO@PAni towards Cr (VI) was observed, and the obtained results are summarized in Figure 8a. The observations demonstrated that with the increase in nanorod size from 10.5 to 20 nm, adsorption capacity of ZnO@PAni increased from 25.56 to 37.93 mg/g, while beyond 20 nm, a gradual decrease in adsorption capacity was observed, which might be due to the lower surface area possessed by larger-size nanorods [66]. Initially, at 10.5 nm nanorod size, there may be a possibility of stacking of nanorods due to small size and hence a lower

of agglomeration decreased, and as a result, nanorods were distributed throughout the aqueous media and thus a high adsorption capacity was achieved. At a higher particle

*3.4. Adsorption Isotherm Studies* The equilibrium data obtained after experiments were applied to the nonlinear models of Langmuir and Freundlich, and the results are given in Table 2 and Figure 9a,b. Table 2 shows that with a low value of chi square (χ<sup>2</sup> = 3.41) and high value of R<sup>2</sup> (0.99), the Langmuir model was found to be the best model for explaining the adsorption of Cr (VI) on ZnO@PAni. The maximum monolayer adsorption capacity was found to be 142.27 The equilibrium data obtained after experiments were applied to the nonlinear models of Langmuir and Freundlich, and the results are given in Table 2 and Figure 9a,b. Table 2 shows that with a low value of chi square (χ <sup>2</sup> = 3.41) and high value of R<sup>2</sup> (0.99), the Langmuir model was found to be the best model for explaining the adsorption of Cr (VI) on ZnO@PAni. The maximum monolayer adsorption capacity was found to be 142.27 mg/g at 298 K, 219.18 mg/g at 308 K, and 310.47 mg/g at 318 K. The Langmuir constant K<sup>L</sup> was found to be 0.412, 0.767, and 0.849 L/mg at temperatures from 298 to 318 K. The significance

affinity of ZnO@PAni NRs towards Cr (VI) and a high value of adsorption capacity achieved at high temperature. The Freundlich adsorption parameters were calculated from the nonlinear plot between q<sup>e</sup> versus ln C<sup>e</sup> presented in Figure 9b and given in Table 2, which shows that the values of n are >1 for all temperature ranges, indicating good adsorption. The values of n being greater than 1 suggest the existence of a substantial interaction between the ZnO@PAni surface and Cr (VI) ions and the gradual increase in the value of 1 with temperature suggests high favorability of adsorption at high temperature. The obtained results through nonlinear regression for Langmuir and Freundlich models also suggest the data are statistically valid for explaining the adsorption behavior

**Table 2.** Adsorption isotherm parameters for the removal of (100 mg/L) Cr (VI) on ZnO@PAni NRs

**Model Parameters 298 K 308 K 318 K**

q<sup>m</sup> (mg/g) 142.27 219.18 310.47 K<sup>L</sup> (L/mg) 0.412 0.767 0.849 R<sup>2</sup> 0.99 0.99 0.99 χ<sup>2</sup> 4.13 3.55 3.41

K<sup>F</sup> (mg/g) (mg/L)1/n 109.79 111.17 121.44

n 1.84 2.52 3.42 R<sup>2</sup> 0.98 0.97 0.96 χ<sup>2</sup> 21.61 19.04 15.13

mg/g at 298 K, 219.18 mg/g at 308 K, and 310.47 mg/g at 318 K. The Langmuir constant K<sup>L</sup>

of the KL value also establishes the affinity of adsorbent towards adsorbate, and in this case, as the temperature increases, the value of K<sup>L</sup> also increases, suggesting a high affinity of ZnO@PAni NRs towards Cr (VI) and a high value of adsorption capacity achieved at high temperature. The Freundlich adsorption parameters were calculated from the nonlinear plot between q<sup>e</sup> versus ln C<sup>e</sup> presented in Figure 9b and given in Table 2, which shows that the values of n are >1 for all temperature ranges, indicating good adsorption. The values of n being greater than 1 suggest the existence of a substantial interaction between the ZnO@PAni surface and Cr (VI) ions and the gradual increase in the value of 1 with temperature suggests high favorability of adsorption at high temperature. The obtained results through nonlinear regression for Langmuir and Freundlich models also suggest the data are statistically valid for explaining the adsorption behavior of ZnO@PAni NRs towards Cr (VI) [69,70].


**Table 2.** Adsorption isotherm parameters for the removal of (100 mg/L) Cr (VI) on ZnO@PAni NRs at 298 K, 308 K, and 318 K.

**Figure 9.** Nonlinear regression fitted adsorption isotherms of (**a**) Langmuir and (**b**) Freundlich using 20 mL of 100 mg/L Cr (VI) solution at pH 2 and 13 mg of adsorbent dose at 298–318 K temperature range*.* **Figure 9.** Nonlinear regression fitted adsorption isotherms of (**a**) Langmuir and (**b**) Freundlich using 20 mL of 100 mg/L Cr (VI) solution at pH 2 and 13 mg of adsorbent dose at 298–318 K temperature range.

#### *3.5. Adsorption Kinetics 3.5. Adsorption Kinetics*

20 40 60 80 100 120 140 160 180

Contact Time (min)

5

10

15

20

25

**(a)**

qt (mg/g)

30

35

40

The kinetic data obtained from the kinetic studies are listed in Table 3, and graphs are given in Figure 10a,b. The high value of correlation coefficients R<sup>2</sup> = 0.99 and low value of χ<sup>2</sup> = 0.36 characterized the pseudo-second-order kinetic model best describing the sorption rate of Cr (VI) on the surface of ZnO@PAni. According to the pseudo-second-order kinetic model, chemical adsorption may be the rate-limiting phase, and there may be entitlement of valence forces through the electron deficient Cr (VI) orbitals and electron rich -NH<sup>2</sup> and -OH groups, which can involve a complex reaction involving chemical bond The kinetic data obtained from the kinetic studies are listed in Table 3, and graphs are given in Figure 10a,b. The high value of correlation coefficients R<sup>2</sup> = 0.99 and low value of χ <sup>2</sup> = 0.36 characterized the pseudo-second-order kinetic model best describing the sorption rate of Cr (VI) on the surface of ZnO@PAni. According to the pseudo-second-order kinetic model, chemical adsorption may be the rate-limiting phase, and there may be entitlement of valence forces through the electron deficient Cr (VI) orbitals and electron rich -NH<sup>2</sup> and -OH groups, which can involve a complex reaction involving chemical bond formation

formation and diffusion reactions simultaneously [71]. Moreover, the obtained q<sup>e</sup> (calculated values) for PSO are found to be ore closer to the q<sup>e</sup> (experimental values). According

fusion process, then the plot between qt and t0.5 will be a straight line. Figure 10b represents the intraparticle diffusion plot between qt and t0.5 which showed a multilinear plot which does not pass through the origin, suggesting the involvement of boundary-layer diffusion in Cr (VI) adsorption. The first linear portion with a kp1 value of 5.02 represents the rapid external diffusion of Cr (VI) to the ZnO@PAni surface and second portion with

5

10

15

20

25

30

35

40

 Experimental Data Linear Plot 1 Linear Plot 2

2 4 6 8 10 12 14

**(b)**

t1/2

kp2 value of 1.48 corresponds to the intraparticle diffusion effect.

qt (mg/g)

Experimental Data Pseudo First Order Pseudo Second Order Elovich

**qe**

**(mg/g)**

10

20

30

40

50

60

70

80 **298 K**

 **318 K**

and diffusion reactions simultaneously [71]. Moreover, the obtained q<sup>e</sup> (calculated values) for PSO are found to be ore closer to the q<sup>e</sup> (experimental values). According to The Weber–Morris equation, if the adsorption rate is controlled by the intraparticle diffusion process, then the plot between qt and t0.5 will be a straight line. Figure 10b represents the intraparticle diffusion plot between qt and t0.5 which showed a multilinear plot which does not pass through the origin, suggesting the involvement of boundary-layer diffusion in Cr (VI) adsorption. The first linear portion with a kp1 value of 5.02 represents the rapid external diffusion of Cr (VI) to the ZnO@PAni surface and second portion with kp2 value of 1.48 corresponds to the intraparticle diffusion effect. 0.0 0.1 0.2 0.3 0.4 0.5 **C<sup>e</sup> (mg/L)** 0.0 0.1 0.2 0.3 0.4 0.5 20 30 40 50 qe (mg/g)C<sup>e</sup> (mg/L)

60

70

<sup>80</sup> 298K 308K 318K **(b)**

*Water* **2023**, *15*, x FOR PEER REVIEW 14 of 21

**(a)**

**Table 3.** Kinetic parameters for the removal of 50 mg/L Cr (VI) on ZnO@PAni NRs at optimized reaction condition. **Figure 9.** Nonlinear regression fitted adsorption isotherms of (**a**) Langmuir and (**b**) Freundlich using 20 mL of 100 mg/L Cr (VI) solution at pH 2 and 13 mg of adsorbent dose at 298–318 K temperature range*.*


**Figure 10.** (**a**) Nonlinear fitted kinetic curves for pseudo-first order, pseudo-second order, and Elovich model, and (**b**) linear plot of intraparticle diffusion.

#### *3.6. Adsorption Thermodynamics*

Table 4 and Figure 11 provide a list of the thermodynamic characteristics related to the adsorption of Cr (VI) on the ZnO@PAni surface.


**Table 4.** Thermodynamic parameters for the removal of Cr (VI) on ZnO@PAni NRs at 298 K, 308 K, and 318 K. Intraparticle diffusion R1 <sup>2</sup> 0.98 R2 <sup>2</sup> 0.96

**Figure 10.** (**a**) Nonlinear fitted kinetic curves for pseudo-first order, pseudo-second order, and Elo-

**Table 3.** Kinetic parameters for the removal of 50 mg/L Cr (VI) on ZnO@PAni NRs at optimized

**Model Parameters 318 K**

qe,cal (mg/g)

qe,cal (mg/g)

qe,exp (mg/g) 35.69

<sup>k</sup><sup>1</sup> (1/min) 41.36 R<sup>2</sup> 0.98 χ<sup>2</sup> 1.63

qe,exp (mg/g) 35.69

<sup>k</sup><sup>2</sup> (g/mg × min) 36.27

α (mg/g × min) 1.64 β (g/mg) 0.089 R<sup>2</sup> 0.96 χ<sup>2</sup> 2.36

kp1 (mg/g × min1/2) 5.02 kp2 (mg/g × min1/2) 1.48

R<sup>2</sup> 0.99 χ<sup>2</sup> 0.36

the adsorption of Cr (VI) on the ZnO@PAni surface.

*Water* **2023**, *15*, x FOR PEER REVIEW 15 of 21

vich model, and (**b**) linear plot of intraparticle diffusion.

reaction condition.

Pseudo-first order

Pseudo-second order

Elovich

**Figure 11.** Adsorption thermodynamics curve for evaluation of enthalpy and entropy of the reac-**Figure 11.** Adsorption thermodynamics curve for evaluation of enthalpy and entropy of the reaction.

tion*.* Table 4 shows that the value of ∆H◦ is found to be 32.01 kJ/mol, which suggests that the adsorption of Cr (VI) on ZnO@PAni is endothermic in nature. Owing to this explanation, the adsorption reaction is highly favorable at a high temperature, i.e., 318 K, and less favorable at 298 K. The entropy ∆S ◦ was found to be 0.187 kJ/mol × K, which is also positive, suggesting a randomness created in the system. The Gibbs free energy value ∆G◦ was found to be −23.79 kJ/mol at 298 K, −25.67 kJ/mol at 308 K, and −27.54 kJ/mol at 318 K, which suggests the feasible nature of the adsorption reaction and increase in negative magnitude with temperature, suggesting high feasibility at high temperature [72].

#### *3.7. Adsorption Mechanism*

From the kinetic and isotherm studies, it was observed that both adsorbent and adsorbate govern the adsorption process and as the time progresses, more and more portions of surface-active sites on the adsorbent surface are occupied accordingly. The pseudo-second-order model revealed the primary rate-controlling step as the adherence of Cr (VI) on the surface of ZnO@PAni. Additionally, the intraparticle diffusion also suggested the involvement of boundary-layer diffusion in Cr (VI) adsorption. The first step is ascribed as the mass transfer of Cr (VI) to the adsorbent surface, and the second step is attributed as the diffusion process through the inner nanopores of adsorbent. From the FTIR and SEM-EDX analysis, it was confirmed that the adsorbent surface is rich with electron-donating functional groups such as -OH and -NH<sup>2</sup> groups, which are involved in the adsorption process by the following Equations (13) and (14). Figure 1f shows the obtained EDX data of Cr (VI)-adsorbed ZnO@PAni. The EDS data reveal the presence of Cr, C, Zn, and O elements. Thus, it is confirmed that Cr (VI) particles are present on the ZnO@PAni surface. This confirms the adsorption properties of ZnO@PAni nanocomposite.

$$\text{R-OH} + \text{HCrO}\_4^- + \text{H}^+ \text{R-(OH}\_2\text{)}^+/\text{HCrO}\_4^- \tag{13}$$

$$\text{R-NH}\_2 + \text{HCrO}\_4^- + \text{H}^+\text{R-(NH}\_3\text{)}^+/\text{HClO}\_4^- \tag{14}$$

#### *3.8. Comparison with the Literature*

The adsorption studies of the synthesized material ZnO@PAni were compared with other ZnO-based nanocomposite materials reported in the literature and are listed in Table 5. It was observed from the comparison data that modification of ZnO with PAni results in enhanced adsorption capacity towards Cr (VI) as compared to other materials.


**Table 5.** Comparison of the adsorption performance of present study with literature.

#### **4. Conclusions**

In summary, a multifunctional nanocomposite material using PAni and ZnO nanorods was synthesized through a free radical oxidative polymerization reaction. Various spectroscopic and morphological studies confirmed the successful formation of the material. The FTIR studies suggested the formation of H bonding between ZnO and -NH<sup>2</sup> groups of PAni. The XRD results for ZnO@PAni demonstrated the presence of the (200) diffraction plane of PAni with characteristic peaks of ZnO. The TEM image of the synthesized ZnO/PAni composite showed that ZnO nanowires are linked to the flakelike PAni with an average particle size of 22 nm. The synergistic effect of solid ZnO NRs in the polymer matrix results in high thermal stability of PAni. The BET studies showed that ZnO@PAni has a high surface area of 113.5 m2/g with average pore width of 7 nm. The material as an adsorbent showed high affinity towards Cr (VI) from an aqueous solution at optimized reaction conditions of pH 2 and 13 mg adsorbent dose. The adsorption reaction was highly pH dependent and the obtained data were best explained by the Langmuir model, resulting in maximum monolayer adsorption capacity (qm), as 142.27 mg/g at 298 K, 219.18 mg/g at 308 K, and 310.47 mg/g at 318 K were obtained. The kinetic studies suggested the uptake of Cr (VI) by ZnO@PAni NRs was best supported by a pseudo-second-order model, indicating a chemical bond formation between Cr (VI) and the adsorbent surface. In future, we plan to utilize the adsorptive and optical properties of the material towards Cr (VI)-polluted wastewater, splitting the simultaneous hydrogen production reaction under UV-Vis light radiations. Thus, the synthesized nanomaterial could emerge as cost-effective and highly efficient towards sustainable wastewater management and energy production.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/w15101949/s1, Figure S1: Point of zero charge (pHpzc) analysis using Zeta potential; Figure S2: Non-linear regression for (a) Langmuir (b) Freundlich and (c) Temkin isotherms at 298 to 318 K temperature. Table S1: Adsorption isotherm parameters for the removal of (100 mg/L) Cr (VI) on ZnO@PAni NRs at 298 K, 308 K and 318 K obtained by non-linear regression analysis.

**Author Contributions:** Conceptualization, F.A.A.; Methodology, F.A.A.; Software, F.A.A., R.H.A. and I.H.; Validation, I.H.; Formal analysis, R.H.A.; Investigation, R.H.A.; Resources, R.H.A.; Data curation, I.H.; Writing—original draft, R.H.A.; Writing—review & editing, I.H.; Supervision, F.A.A.; Project administration, F.A.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Data is contained within the article.

**Acknowledgments:** The authors extend their thanks to Researchers Supporting Project (Ref: RSP2023R442), King Saud University, Riyadh, Saudi Arabia.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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**Naila Farid 1, Amin Ullah 1,\*, Sara Khan 1, Sadia Butt 2, Amir Zeb Khan 3, Zobia Afsheen 1, Hamed A. El-Serehy <sup>4</sup> , Humaira Yasmin 5,6, Tehreem Ayaz <sup>7</sup> and Qurban Ali 8,\***


**Abstract:** Aquatic bodies contaminated by heavy metals (HMs) are one of the leading issues due to rapidly growing industries. The remediation of using algae and hydrophytes acts as an environmentally friendly and cost effective. This study was performed to investigate the pollution load, especially HMs, in the wastewater of the Gadoon Industrial Estate and to utilize the hydrophytes (*Typha latifolia* (TL) and *Eicchornia crassipes* (EI)) and algae (*Zygnema pectiantum* (ZP) and *Spyrogyra* species (SS)) as bioremediators. The wastewater was obtained and assessed for physiochemical parameters before treating with the selected species. The pot experiment was performed for 40 days. Then the wastewater samples and selected species were obtained from each pot to analyze the metal removal efficiency and assess for metal concentrations using atomic absorption spectrophotometry. The dissolved oxygen (DO; 114 mg/L), total suspended solids (TSS; 89.30 mg/L), electrical conductivity (EC; 6.35 mS/cm), chemical oxygen demand (COD) (236 mg/L), biological oxygen demand (BOD; 143 mg/L), and total dissolved solids (TDS; 559.67 mg/L), pH (6.85) were analyzed. The HMs were noted as Zn (5.73 mg/L) and Cu (7.13 mg/L). The wastewater was then treated with the species, and significant reductions were detected in physicochemical characteristics of the wastewater such as DO (13.15–62.20%), TSS (9.18–67.99%), EC (74.01–91.18%), COD (25.84–73.30%), BOD (21.67–73.42%), and TDS (14.02–95.93%). The hydrophytes and algae removed up to 82.19% of the Zn and 85.13% of the Cu from the wastewater. The study revealed that the hydrophytes and algae significantly decreased the HM levels in the wastewater (*p* ≤ 0.05). The study found TL, EI, ZP, and SS as the best hyper accumulative species for Zn and Cu removal from wastewater. The HMs were removed in the order of Cu > Zn. The most efficient removal for Cu was found by *Typha latifolia* and Zn by *Zygnema pectiantum*. It was concluded that bioremediation is an environmentally friendly and cost-effective technique that can be used for the treatment of wastewater due to the efficiency of algae and hydrophytes species in terms of HM removal.

**Keywords:** bioremediation; hydrophytes; algae; heavy metals; wastewater

#### **1. Introduction**

Industrial wastewater (IWW) is considered one of the significant sources of aquatic contamination [1]. Due to the important use of heavy metals (HMs) in industrial processes, different poisonous HMs are widely used in many industrial activities such as

**Citation:** Farid, N.; Ullah, A.; Khan, S.; Butt, S.; Khan, A.Z.; Afsheen, Z.; El-Serehy, H.A.; Yasmin, H.; Ayaz, T.; Ali, Q. Algae and Hydrophytes as Potential Plants for Bioremediation of Heavy Metals from Industrial Wastewater. *Water* **2023**, *15*, 2142. https://doi.org/ 10.3390/w15122142

Academic Editor: Hai Nguyen Tran

Received: 7 April 2023 Revised: 29 May 2023 Accepted: 29 May 2023 Published: 6 June 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

mining, smelting, plating, fertilizer manufacturing, the textile, chemical, plastics, and pigments industries, etc. [2,3]. Heavy-metal-contaminated water is a serious issue due to the non-degradable nature of HMs and their tendency to accumulate in natural water bodies [4–6]. When the metals are discharged in the water bodies, they settle down and enter the aquatic bodies, leading to severe health problems and even causing the death of aquatic organisms [7].

The conventional methods used for metal removal from wastewater include electrochemical methods (electrocoagulation, electroflotation, and electrodeposition), coagulation– flocculation, floatation, membrane filtration, ion exchange, and chemical precipitation [8]. However, these methods have the drawbacks of maintenance costs, expensive operation, and secondary contamination due to the formation of toxic sludge. For landfill leachate treatment, constructed wetlands may be regarded as more environmentally friendly and sustainable solutions. Constructed wetlands have been used to remove pollution from various wastewater streams. In constructed wetlands, wastewater is remediated by physical (e.g., filtration and sedimentation) and chemical processes (e.g., adsorption and precipitation), as well as biological processes (e.g., uptake from the water column and root zone and microbial degradation) [9]. Constructed wetlands have been used to treat a wide range of storm water runoff, waste streams, landfill leachate, agricultural drainage including mine drainage, domestic wastewater, as well as industrial effluents [9,10]. Plants induce physicochemical and biological processes to remediate metals from wastewater effluents [11], which help in its treatment [12]. Hydrophytes such as Pistia stratiotes [13], Lemna gibba [14], and *Eichhornia crassipes* [12] are free-floating and known for pollution absorption particularly for HMs in polluted water. Similarly, other classes of emergent species such as *Typha latifolia* [15] have the capability of metal accumulation in higher concentration in shoots and roots [16]. According to the study of Khan et al. [17] regional species of hydrophytes are adapted to local climatic conditions and appropriate to use in constructed wetlands for the treatment of wastewater. So, one of the most effective and best substitutes for the conventional methods is bioremediation, which uses biological materials to convert toxic metals into less harmful substances [18,19]. Bioremediation is a cost-effective and environmentally friendly method in the revitalization of the environment [20,21]. There are two main approaches to bioremediation. Phytoremediation (plant-centered) is a technique that uses either genetically modified or raw plants to restore polluted water and land sources [22]. Some hydrophytes such as water cabbage (*Pistia stratiotes*), duckweed (*Lemna gibba*) [14], and water hyacinth (*Eichhornia crassipes*) [12] are floating hydrophytes famous for pollution absorption capability mostly for metals in IWW.

Phycoremediation is a valuable technique used to remediate metal-polluted water. The algae efficiently remove metals from wastewater [23,24]. In living algal cells, metal absorption occurs through binding with the intracellular ligands and the cell surface. The metal-bonded ligands accumulate further by active biological transport [25]. The metal removal mechanisms rely on various functional groups such as hydroxyls, amines, and carboxylates forming complexes with metal [26] and decreasing metal concentrations in the treated water. Earlier, studies have verified that algal species improve the wastewater quality and IWW by metal absorption [24,27,28]. Moreover, the microalgal biomass after bioremediation can be processed further to produce biochar, biodiesel, value-added products, and metal extraction, representing the process as environmentally sustainable and cost effective. Algae gained the attention of researchers as a potential applicant for biodiesel production among different feedstock. Microalgae cultivation can use non-agricultural land, suggesting that the land requirement for algae biodiesel production is low [29]. Algae can utilize CO2 as a carbon source for oil (i.e., biodiesel) and biomass production. Algae have rapid growth potential and have oil contents up to 50% dry weight of biomass [30]. The waste biomass can be converted into biochar, which plays a crucial role in protecting and repairing the environment [31].

In Pakistan, it is alarming that most industries and cities lack facilities for wastewater treatment. Huge industrial effluents are being directly released to surface water, resulting

in severe pollution. Due to organic loads and toxic materials such as HMs, the IWW forms a main source of aquatic pollution in Pakistan. It causes water to be unsuitable for drinking, irrigation, or any other use. HM pollution has become a severe risk to living organisms in the environment. It can cause different health issues such as lung insufficiency, nervous disorders, bone injury, cancer, teratogenic and embryotoxic effects, and hypertension in humans. To deal with this issue, a variety of methods can be utilized, but these methods are expensive and badly affect the environment. In developing countries such as Pakistan, these methods cannot be used to remove HMs. Therefore, it is expected that hydrophytes and algal species may contribute in reclaiming the wastewater discharged from the Gadoon Industrial Estate, Pakistan. This study aims to assess the physiochemical parameters of the wastewater collected from the Gadoon Industrial Estate and the removal efficiency of the HMs and to compare the HM removal efficiency of algae and hydrophytes.

#### **2. Materials and Methods**

#### *2.1. Algae and Hydrophyte Collection and Transplantation*

In the study, the algal species *spirogyra spp.* and *Zygnema pectinatum* were collected from the Khiyali River, Peshawar. The new buds of *Typha latifolia* (cattail) and *Eichhornia crassipes* (water hyacinth) were collected from the ponds in Sabi village, District Peshawar. The species were first washed with tap and distilled water. The algae were then observed and identified by the procedure adopted by [32]. To remove dust and sand, the selected species were washed thoroughly and acclimatized and transplanted into IWW for 40 days at room temperature (18–20 ◦C). Figure 1 shows the collection of hydrophytes and algae.

**Figure 1.** Collection sites of selected hydrophytes and algae.

#### *2.2. Collection and Analysis of IWW*

The IWW was obtained from the Gadoon Industrial Estate (34◦7 8 N 72◦38 45 E) using the procedure mentioned by Ayaz et al. [15]. The wastewater sample was obtained during the month of May with average temperatures ranging from 78 to 103 F◦ and an average rainfall of 3.3 inches. The Gadoon Industrial Estate has many different industrial units such as the steel, chemical, soap and oil, textile, marble, and ghee industries. The study area consists topographically of flood deposits and uneven natural and ground drains and contains widespread gravel deposits with shingle beds and clay and silt stratifications,

generally flat in the west and south of the industrial estate. The solid waste substances were removed [32]. Then the IWW was analyzed for physiochemical characteristics such as temperature, TDS, electrical conductivity (EC), pH, TSS, DO, BOD, COD, and metals (Cu and Zn). The metals were then analyzed by atomic absorption spectrophotometry in the Central Resource Laboratory, University of Peshawar.

#### *2.3. Experimental Design*

The pot experiment was performed to observe the efficiency of metal (Cu and Zn) removal by individual hydrophytes and algal species (Figure 2). For this experiment, four clean pots were first rinsed using double-deionized distilled water and 10% HNO3 (diluted nitric acid). The treatment pots for hydrophytes were then marked as TL (containing 50 g *Typha latifolia*) and EI (containing 50 g *Eichhornia crassipes*) and were provided with 20 L of IWW. Similarly, the algal pots were named as ZP (containing 15 g *Zygnema pectinatum*) and SS (containing 15 g *Spirogyra* spp.) provided with 5 L of wastewater sample for transplantation. The hydrophytes were transplanted in sediments containing IWW. All four pots were placed at room temperature (27 ◦C) under natural light/dark 14:10 conditions for 40 days. After experimentation, species and water samples were obtained from all containers and were analyzed for different physiochemical characteristics and metals (Cu and Zn).

**Figure 2.** Experimental pots at initial and final stage.

#### *2.4. Sampling and Analysis of Sediment*

For the transplantation of the hydrophytes, clean sediment was utilized in each pot (5 kg). Before experimentation, triplicate samples of the sediment were obtained and analyzed from TL and EI containers. The samples were observed for HMs (Zn and Cu) and various physiochemical characteristics. Temperature, EC, and pH were determined in the sediment samples (10 g) using an EC meter (InoLab level-1) and a pH Meter (Model: pH-208). Using the Australian standard procedure adopted by Ayaz et al. [15], the soil moisture content (MC) was determined, whereas the TOM (total organic matter) was observed using the method adopted by Walkley and Black [33]. On the ignition method, through weight loss, the TOC (total organic carbon) was calculated [34]. For the MC, TOM, and TOC, 50 g of sediment sample was used. In sediment, (0.5 g) metals were extracted using a wet digestion procedure [35] and quantified by atomic absorption spectrophotometry (AAS-700 PerkinElmer: Norwalk, CT, USA) in the Central Resource Laboratory, University of Peshawar.

Water samples were collected from the TL, EI, ZP, and SS containers at the end of the experiment. The samples were examined for physicochemical parameters and HMs (Zn and Cu). A quantity of 50 mL of water samples were taken, and the physicochemical

characteristics such as EC, pH, and temperature were observed using the EC meter (InoLab-WTB GmbH; Weilheim, Germany) and the pH Meter (Model: pH-208), respectively. The TDS and TSS were determined for the water sample (10 mL) using the gravimetric method. The DO, BOD5, and COD were observed for the water sample (200 mL titrametrically by standard procedure adopted by the American Public Health Association [36]). The samples were also examined using atomic absorption spectrophotometry for HMs (Zn and Cu).

#### *2.5. Sampling, Preparation, and Analysis of Hydrophyte*

After experimentation, samples of TL, EI, ZP, and SS were collected in triplicate and adequately labeled to observe the metal removal efficiency of selected species. The root and aerial parts of the TL and EI were processed separately for further analysis using standard methods [17]. The harvested species samples were washed adequately to remove dust, clay, sand, and particles. The samples were then dried at 70 ◦C in an oven and then ground and stored in polythene bags for wet digestion [35]. After wet digestion, the extracted samples were quantified for heavy metals using atomic absorption spectrophotometry.

#### *2.6. Formula*

#### 2.6.1. Bioconcentration Factor

The bioconcentration factor (*BCF*) of algal and hydrophyte efficiency for metal accumulation from the wastewater samples was determined using Equation (1) [37,38].

$$BCF\left(\%\right) = \frac{\mathcal{C}\_{alga\mathcal{e}}}{\mathcal{C}\_{water}} \times 100\tag{1}$$

where *Calgae* refers to the metal concentration in the algae and *Cwater* is the metal concentration in the water.

#### 2.6.2. Translocation Factor

The translocation factor (TF) was used to measure the metals translocated from wastewater and accumulated in the hydrophyte's tissues using Equation (2) as below:

$$T\mathbb{C}F(\%) = \frac{\mathbb{C}\_{aerial}}{\mathbb{C}\_{root}} \times 100\tag{2}$$

*Caerial* refers to the metals accumulated in the aerial parts, and *Croot* refers to the metal concentrations in the root part.

#### 2.6.3. Bioaccumulation Measurement

The bioaccumulation capacity (*q*) was determined using Equation (3) as given below [39].

$$q = \frac{\mathbf{C}\_i - \mathbf{C}\_f}{M} \times V \tag{3}$$

*Ci* is the initial metal concentration, *Cf* is the final metal concentration, *M* is the amount of algal or hydrophyte dry biomass (g), and *V* is the water volume in (L).

#### 2.6.4. Bioremoval Efficiency

The bioremoval efficiency (%) was measured using Equation (4) [24].

$$R = \frac{\mathcal{C}\_i - \mathcal{C}\_f}{\mathcal{C}\_i} \times 100\tag{4}$$

*R* refers to the removal percentage, *Ci* is the initial metal concentration in wastewater, and *Cf* is the final metal concentration in wastewater.

#### *2.7. Statistical Analysis*

The statistical analysis was carried out as follows: Microsoft Excel and the statistical package for social sciences (SPSS) was used. ANOVA and *t*-test were applied for the significance between the variables of the parameters.

#### **3. Results and Discussion**

#### *3.1. Physicochemical Characteristics of Sediment Sample*

Table 1 summarizes the physicochemical analysis of sediments. The results showed lower concentrations of Zn and Cu (5.30 and 19.20 mg/kg) below the maximum permissible limit set by Pak-EPA (Pakistan Environmental Protection Agency, 2008) before starting the experiment. However, the levels of Zn and Cu raised to 7.47 and 20.82 mg/kg in the pots, respectively, ensuring the sedimentation and presence of metals from the water samples. Mass balancing suggested that the HMs were primarily deposited in sediments and hydrophytes. This character was also observed in the research studies reported by Hadad et al. [40] and Mays and Edwards [41].


**Table 1.** Physiochemical parameters of sediment sample.

\* Initial: samples at the initial stage of the experiment. Final: samples at the final stage of the experiment.

#### *3.2. Characteristics and Pollution Load of IWW*

The physiochemical parameters' values of IWW are given in Supplementary Materials Table S1. The IWW collected from all four containers, including TL (treatment container for *Typha latifolia*), EI (treatment container for *Eicchornia crassipes*), ZP (treatment container for *Zygnema pectinatum*), and SS (treatment container for *Spyrogyra* spp.), was observed before and after the treatment or experimentation for parameters such as EC, pH, TSS, BOD, temperature, TDS, DO, and COD.

The pH (6.85) observed for IWW was within the maximum permissible limit set by Pak-EPA. The EC, DO, BOD, COD, TSS, and TDS values determined for IWW were 6.35 mS/cm, 114 mg/L, 143 mg/L, 236 mg/L, 89.30 mg/L, and 559.67 mg/L. The TSS and TDS values were analyzed below the Pak-EPA limit (150 mg/L and 3500 mg/L, respectively). However, the COD and BOD values exceeded the limits (150 mg/L and 80 mg/L, respectively) as set by Pak-EPA. The concentrations of Zn (5.73 mg/L) and Cu (7.13 mg/L) in the IWW exceeded the maximum permissible limit of Pak-EPA.

Previously, similar findings (pH 7.6) were reported by Fito et al. [42], who carried out a study on the wastewater of the sugar industry. The EC results agreed with the results (320 S/cm) of Asia and Akporhonor [43], who analyzed the physicochemical characteristics of IWW from the rubber industry. The present TSS value was primarily similar to the findings (43 mg/L) reported by Shamshad et al. [24]. Hossain et al. [44] investigated the physical and chemical parameters of the IWW discharged from various industries (Bangladesh) and observed similar findings (EC: 2.64 mS/cm). Similarly, the present values conformed with the EC (0.24–5.04 mS/cm), DO (341 mg/L), BOD (143 mg/L), and COD values of the IWW in a research study reported by Ayaz et al. [15], a phytoremediation study performed on the Hayatabad Industrial Estate. However, the results were different from the EC (149.1–881.3 mS/cm), TSS (2470 mg/L), and COD (1231 mg/L) values observed by Aniyikaiye et al. [45] in a study conducted on the IWW released from the Nigerian paint industries. The present results were much different from the DO (1.83 mg/L) and BOD (25 mg/L) values reported on the phycoremediation of the wastewater of the Hayatabad Industrial Estate performed by Khan et al. [46]. Similarly, Singh et al. [47] carried out a similar study on the textile industry of Ludhiana, India, using wastewater collected from different dyeing mills, and higher COD (3050 mg/L) and BOD values (790 mg/L) were reported. Similarly, the TSS finding (397.5 mg/L) of the research study of Abrha et al. [48] on the IWW obtained from Ethiopian beverage industries was much different from this study's findings. In another study performed on treatments for removing pharmaceutical wastes from wastewater, Badawy et al. [49] reported high TDS values. Higher values of TDS (7072 mg/L) in the textile industry wastewater have been reported by Paul et al. [50]. The differences in the findings can be accredited to the differences in sampling sites and study areas.

#### *3.3. Effect of Hydrophytes and Algal Species on Physicochemical Parameters*

The effects of hydrophytes and algae on various characteristics of the water samples are shown in the Supplementary Materials Table S1. The results revealed that *Typha latifolia* had significantly (*p* < 0.05) reduced EC (91.18%), TSS (50.94%), TDS (14.02%), DO (13.15%), BOD (21.67%), and COD (25.84%) during 40 days. In a recent study, the effect of *Eicchornia crassipes* was found lower on EC (77.48%) and TSS (9.18%) and higher on TDS (95.93%), DO (60.52%), BOD (73.42%), and COD (73.30%) than *Typha latifolia*. Similarly, the algal species *Zygnema pectinatum* had also significantly (*p* < 0.05) decreased the pollution of IWW such as EC (74.01%), TSS (63.04%), TDS (75.84%), DO (18.42%), BOD (38.46%), and COD (38.55%) during 40 days. In a recent study, the effect of *Spyrogyra* spp. was found to be lower on TDS (73.51%), BOD (29.37%), and COD (26.27%) and higher on EC (80.31%), TSS (67.99%), and DO (62.20%) than *Zygnema pectinatum* (Supplementary Materials Table S1). The results are consistent with the results reported by Khan et al. [46], the research conducted for the remediation of the IWW of the Hayatabad Industrial Estate by algae (*Oedogonium westi*, *Cladophora glomerata*, *Zygnema insigne*, and *Vaucheria debaryana*) who recorded a significant decrease in the COD (30.7%), EC (85.9%), TDS (79%), and BOD (52.4%). However, these findings were different from the findings reported by Sharma et al. [51] in their study on *Chlorella minutissima*, and significant reductions in the COD (80.5%), TDS (94.4%), and BOD (93%) of the IWW were detected. Similarly, the results of this study were inconsistent with the findings of Okpozu et al. [52], whose study reported on the bioremediation of cassava IWW by *Desmodes musarmatus* and detected a reduction in the COD by 92% and the BOD by 87%. The differences in the results could be linked to the wastewater characteristics, species of algae, and nutrients. Okpozu et al. [52] transplanted the algae in Bold's basal medium and hydrolyzed cassava wastewater.

#### *3.4. Effects of Selected Species on Heavy Metals* 3.4.1. Zinc (Zn)

The average or mean of the Zinc concentration at the initial stage of the IWW was recorded as 5.73 mg/L. The mean Zn concentrations observed in the water samples collected after 40 days of the experiment from TL, EI, ZP, and SS were 1.63, 2.17, 1.02, and 2.24 mg/L, respectively (Figure 3). The efficiency of the Zn removal was noted as ranging from

60.90 to 82.19%, where 71.55, 62.12, 82.19, and 60.90% was removed by TL, EI, ZP, and SS, respectively, as shown in Table S1. The t-test results showed that TL, EI, ZP, and SS significantly (*p* ≤ 0.05) decreased the Zn level in the final stage water samples.

**Figure 3.** Concentration of zinc in water samples.

The Zn accumulation in the hydrophyte species differs in selected species, and the Zn level was observed to be the maximum in the underground parts of the hydrophyte species compared with the upper or aerial parts. The aerial parts of the TL and EI uptake were 71.23 and 33.61 mg/kg, respectively. The maximum uptake by the aerial tissues of the hydrophyte species was determined (71.23 mg/kg) in TL, and the minimum uptake (33.61 mg/kg) was determined in EI. The Zn uptakes by the root tissue of TL and EI were discovered to be 105.41 and 59.05 mg/kg, respectively (Figure 4). The Zn concentration recorded in the algal biomass after 40 days of experimentation is shown in Figure 5.

**Figure 4.** Heavy metal concentrations in hydrophytes.

The translocation factors or bioconcentrations factor determined for TL, EI, ZP, and SS for Zn were 67.57, 56.91, 76.26, and 52.70%, respectively (Figure 6). The ZP was recorded for the highest and the SS was recorded for the lowest bioconcentration factor, as shown in Figure 5. The bioaccumulation capacities (q) of TL, EI, ZP, and SS for Zn were 1.64, 1.42, 1.57, and 1.16 mg/g. TL was observed as having the highest and SS was observed as having the lowest bioaccumulation capacity (Figure 7). The bioremoval efficiencies recorded for TL, EI, ZP, and SS were 71.55, 62.12, 82.19, and 60.90%, respectively. The highest Zn bioremoval efficiency was determined for ZP, and lowest was determined for SS (Supplementary Materials Table S1).

**Figure 7.** Bioaccumulation of heavy metals in hydrophytes and algae.

However, the algal bioaccumulation values did not support the results (0.745–1.286 mg/kg) of Khan et al. [46]. The bioaccumulation capacity variation may have been due to the different algal species used for bioremediation or the differences in metal concentrations in the IWW collected. The results obtained from the hydrophytes agreed with the results (98.8–99.3%) of the research study presented by Kanagy et al. [53]. Moreover, the findings were much higher than those obtained from the constructed wetland remediating IWW in the central Mediterranean [54]. The present results were much high than those in the literature (48.3%) reported by Khan et al. [17]. The t-test showed that the Zn level in the IWW samples was more reduced (*p* ≤ 0.05) than the initial samples of TL, EI, ZP, and SS, suggesting the efficient removal of Zn by selected hydrophytes from the IWW.

The comparative study of the bioremoval efficiency, bioaccumulation capacity, and bioconcentration factor observed for hydrophyte treatments (TL and EC) revealed that *Typha latifolia* was the most effective species for the remediation of Zn as compared with *Eicchornia crassipes*. Our study's findings aligned with the literature presented by Ayaz et al. [15], revealing that *Typha latifolia* was more effective in metal removal from IWW than *Eicchornia crassipes*. Similarly, the comparative study of removal efficiency, bioaccumulation capacity, and bioconcentration factor observed for algal treatments by ZP and SS revealed that *Zygnema pectinatum* was the most effective species for Zn remediation as compared with *Spyrogyra* species.

#### 3.4.2. Copper (Cu)

The mean of copper concentration in the initial stage IWW was 7.13 mg/L. The mean Cu concentrations observed in the water samples collected from TL, EI, ZP, and SS were 1.06, 1.37, 1.34, and 1.29 mg/L, respectively, as shown in Figure 8. The maximum Cu level was observed in EI samples, and the lowest value was observed in TL for the final point samples. Efficiencies of Cu removal were noted that ranged from 80.78 to 85.13%, where 85.13, 80.78, 81.20, and 81.90% were removed by TL, EI, ZP, and SS, respectively (Table S1). In a previous study conducted by Mane and Bhosle [55], higher percentages of bioremoval for Cu (89.6%) and Zn (81.53%) were observed by living *Spirogyra* sp. In other phycoremediation studies executed on wastewater, Uddin et al. [56] concluded that 92% of the Cu removal efficiency was observed for *Spirogyra* on ninety minutes of treatments. The *t*-test results showed that TL, EI, ZP, and SS significantly (*p* ≤ 0.05) decreased the Cu level in the final stage water samples compared with the initial stage.

**Figure 8.** The Cu concentration in water.

The Cu bioaccumulation in selected hydrophytes species differed in various species, and the Cu level was observed to be maximum in the roots of the hydrophyte as compared with the aerial tissue. The upper or aerial parts of the TL and EI took up 63.09 and 59.91 mg/kg of Cu, respectively (Figure 3). The maximum uptake by the aerial parts (63.09 mg/kg) was determined in TL, and the minimum uptake (59.91 mg/kg) was determined in EI. The Cu absorption by the roots of TL and EI was determined to be 83.02 and 98.75 mg/kg, respectively. The lowest uptake by the root (83.02 mg/kg) was recorded in TL, and the highest absorption (98.75 mg/kg) was recorded in EI. The Cu uptakes detected in ZP and SS were 5.19 and 5.23 mg/g, respectively (Figure 5). The highest Cu uptake (5.23 mg/g) was observed in SS, and the lowest uptake (5.19 mg/g) was observed in ZP.

The study results indicated the highest uptake of Cu by EI, followed by TL. The analysis showed that Cu concentrations differed in various species. The Cu concentrations were observed to be higher in the roots as compared with the aerial parts of the hydrophytes. The findings of the study agreed with the literature on hydrophytes reported by Kumari and Tripathi [57], Victor et al. [58], and Galal et al. [59].

The translocation factor or bioconcentration factors determined for TL, EI, ZP, and SS for Cu were 75.99, 60.66, 72.79, and 73.35%, respectively. TL was recorded as having the highest and EI was recorded as having the lowest bioconcentration factor (Figure 6). Figure 7 shows the bioaccumulation capacities (q) of TL, EI, ZP, and SS for Cu, which were 2.42, 2.30, 1.93, and 1.94 mg/g. TL was observed as having the highest and ZP was observed as having the lowest bioaccumulation capacity (Figure 7). The bioremoval efficiencies recorded for TL, EI, ZP, and SS were 85.13, 80.78, 81.20, and 81.90%, respectively. The highest Cu bioremoval efficiency was determined for TL, and lowest was determined for EI (Supplementary Materials Table S1).

The comparative study of copper bioremoval efficiency, bioaccumulation capacity, and bioconcentration factors observed for hydrophyte treatment using TL and EI revealed that *Typha latifolia* was the most effective species for the remediation of Cu as compared with *Eicchornia crassipes*. Our study's findings agreed with the study presented by Ayaz et al. [15], showing that *Typha latifolia* more effectively removed Cu from IWW compared with *Eicchornia crassipes.* Similarly, comparing the copper bioremoval efficiency, bioaccumulation capacity, and bioconcentration factors observed for algal treatments revealed that *Spyrogyra* species were the most effective species for Cu remediation as compared with *Zygnema pectinatum*.

#### **4. Conclusions**

The pot experiments concluded that hydrophytes and algae have a key role in the bioremediation of IWW and efficiently reduce metal level in industrial wastewater (IWW). *Zygnema pectinatum* (ZP) had the best Zn removal efficiency, and *Typha Latifolia* was the best bioremediator for Cu. The species removed 85.13% of Cu and 82.19% of Zn. The results recommend that *Typha latifolia*, *Eicchornia crassipes*, *Zygnema pectinatum*, and *Spyrogyra* spp. are the best hyper accumulative species for Zn and Cu from IWW. The heavy metal (Zn and Cu) concentrations of IWW were significantly reduced (*p* ≤ 0.05) after the treatments, which clearly showed the significance of hydrophytes and algae in the removal of metals. Moreover, the selected species survived in the conditions of stress triggered by the metal concentrations. Therefore, this property can be encouraging evidence for hydrophytes and algae to be utilized for remediation. So, the phytoremediation and phycoremediation are inexpensive, environmentally friendly treatment processes that can be utilized for the remediation of IWW contaminated with metals.

**Supplementary Materials:** The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/w15122142/s1: Table S1: Physiochemical parameters of wastewater samples.

**Author Contributions:** Conceptualization, N.F., A.U. and H.Y.; Methodology, A.U. and S.K.; Software, T.A., S.K., A.Z.K. and S.B.; Validation, N.F. and H.A.E.-S.; Formal analysis, A.U., Q.A. and S.B.; Investigation, A.U., A.Z.K., Z.A. and H.Y.; Resources, A.U., Z.A. and S.K.; Data curation, N.F. and A.U.; Writing—original draft, N.F. and S.K.; Writing—review & editing, Q.A.; Supervision, A.U.; Project administration, H.A.E.-S.; Funding acquisition, H.A.E.-S. All authors have read and agreed to the published version of the manuscript.

**Funding:** Researchers Supporting Project Number (RSP2023R19) of King Saud University, Riyadh, Saudi Arabia.

**Data Availability Statement:** The datasets generated during the current study are available from the corresponding author on reasonable request.

**Acknowledgments:** The authors would also like to extend their sincere appreciation to the Researchers Supporting Project Number (RSP2023R19), King Saud University, Riyadh, Saudi Arabia.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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## *Article* **Adsorption-Reduction of Cr(VI) with Magnetic Fe-C-N Composites**

**Xu Liu 1,2,†, Huilai Liu 1,2,†, Kangping Cui 1, Zhengliang Dai 3, Bei Wang 3, Rohan Weerasooriya 2,4 and Xing Chen 1,2,\***


**Abstract:** In this study, the iron-based carbon composite (hereafter FCN-x, x = 0, 400, 500, and 600 calcination) was synthesized by a simple high-temperature pyrolysis method using iron-containing sludge coagulant generated from wastewater treatment settling ponds in chemical plants. The FCN-x was used for the adsorptive reduction of aqueous phase Cr(VI) effectively. The FCN-x was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier infrared spectrometer (FT-IR), X-ray photoelectron spectrometer (XPS), and Brunauer-Emmett-Teller theory (BET). FCN-x adsorption of Cr(VI) was examined in batch experiments using CrO4 <sup>2</sup><sup>−</sup> as a function of physicochemical parameters. The chemical kinetics of Cr(VI) adsorption by FCN-500 were modeled by 1st and 2nd order empirical pseudo kinetics. Based on these experiments, FCN-500 has been selected for further studies on Cr(VI) adsorptive reduction. The maximum Cr(VI) adsorption by FCN-500 was 52.63 mg/g showing the highest removal efficiency. The Cr(VI) adsorption by the FCN-500 was quantified by the Langmuir isotherm. XPS result confirmed the reduction of Cr(VI) to Cr(III) by the FCN-500. The iron-based carbon composites have high reusability and application potential in water treatment. The electroplating wastewater with 117 mg/L Cr(VI) was treated with FCN-500, and 99.93% Cr(VI) was removed within 120 min, which is lower than the national chromium emission standard of the People's Republic of China. This work illustrates the value-added role of sludge generated from dye chemical plants to ensure environmental sustainability.

**Keywords:** magnetic Fe-C-N composite; chromium; adsorption; reduction; coagulation sludge; pyrolyzation

#### **1. Introduction**

The exponential growth of industrial production plants with a concomitant discharge of hazardous wastes into the environment has required urgent attention by regulatory agencies. Electroplating, tanning, painting, printing, textiles, and other industries produce voluminous chromium-laden wastewater discharges [1–3]. As a heavy metal, chromium will cause irreversible damage to human health and the ecological environment [4,5]. In water, mainly trivalent and hexavalent chromium states are common [6,7]. Because Cr(VI) has higher mobility than Cr(III), it is considered to have strong toxicity [8]. Chromium(VI) compounds are potent human carcinogens and are highly genotoxic in bacterial and mammalian

**Citation:** Liu, X.; Liu, H.; Cui, K.; Dai, Z.; Wang, B.; Weerasooriya, R.; Chen, X. Adsorption-Reduction of Cr(VI) with Magnetic Fe-C-N Composites. *Water* **2023**, *15*, 2290. https:// doi.org/10.3390/w15122290

Academic Editor: Hai Nguyen Tran

Received: 9 May 2023 Revised: 9 June 2023 Accepted: 12 June 2023 Published: 19 June 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

cells [9]. Several methods have been developed to remove Cr(VI) in water-based adsorption, biological processes, membrane separation, and advanced water treatment [10–12]. Among them, methods based on adsorption have been widely used due to their low cost, convenient sampling, high removal efficiency, and easy implementation steps [13,14].

The common adsorbents used for chromium ions in water treatment are carbonbased materials, metal-organic frameworks, biomasses, metal oxides, conductive polymers, nanocomposites, and magnetic nanoparticles [5,15–18]. Among them, methods based on magnetic nanoparticles are attractive because of their simple preparation, low cost, and easy separation and recovery [19–22]. Magnetic Fe3O4 without any modification has been used to purify wastewater containing chromium (VI) [23]. However, the bare Fe3O4 is unstable and dissolved in complex environments, thus reducing surface-active sites [24,25]. Different carriers are proposed to fix the bare magnetic Fe3O4 to enhance its stability [26]. Wang et al. (2021) reported sludge as a carrier for Fe3O4 to produce sludge based magnetic Fe3O4 microspheres. The sludge based magnetic Fe3O4 microspheres showed a 118 mg/g maximum adsorption capacity at 323 K. Besides, sludge-based Fe3O4 microspheres are environmentally benign and possesses excellent capabilities for magnetic separation and reduction of Cr(VI) → Cr(III) [27]. Zhao et al. (2016) synthesized magnetic nanocomposites by a single-step reaction using GO, diethylenetriamine, and Fe3O4. AMGO showed efficient adsorption of Cr(VI) and the maximum adsorption amount of Cr(VI) was 123.4 mg/g [28]. Although magnetic nanoparticles have achieved excellent results in Cr(VI) adsorption, the high cost of magnetic nanomaterials often limits their use in environmental remediation, thus requiring more effective and economically feasible production of magnetic adsorbents [29].

Dye chemical wastewater which contains a variety of organic compounds, such as azo, indigo, anthraquinone, aromatic methane, etc., is very complicated [30]. In the past few decades, iron-based chemicals have been widely used as effective coagulants for the purification of dye chemical wastewater and as catalysts for advanced oxidation processes, resulting in the generation of large amounts of sludge containing iron, anthraquinones, benzenes, naphthalenes, and other substances [31,32]. If this sludge is directly discharged or disposed of as waste, it will not only easily cause secondary pollution to the environment, but also be a waste of resources. In this study, the iron-containing coagulant sludge produced in industrial wastewater of a dye chemical plant was calcined at different temperatures under inert conditions. The iron-based carbon material was obtained, which was applied in the Cr(VI) removal experiment. SEM, FTIR, XPS, XRD, and so on were used to characterize the surface structure, morphology, and chemical composition of iron-based carbon materials. The Cr(VI) adsorption on FCN-x was examined as a function of pH, initial adsorbent concentration, equilibrium time, and adsorbate loadings. Besides, an Cr(VI) adsorption mechanism on FCN-500 was developed by coupling kinetic and equilibrium data and spectroscopic measurements. Finally, FCN-500 was used to treat chromium-containing wastewater from the electroplating wastewater plant with successful results.

#### **2. Materials and Methods**

#### *2.1. Chemicals and Reagents*

Analytical grade sulfuric acid (H2SO4), sodium hydroxide (NaOH), hydrochloric acid (HCl), potassium dichromate (K2Cr2O7), phosphoric acid (H3PO4), diphenylcarbazide (C13H14N14O), acetone (C3H6O) were obtained from Sigma-Aldrich (Burlington, MA, USA) and used as received. Iron-containing coagulant sludge was collected from the sedimentation tank of Changhong Chemical Co., Ltd. (Anqing, China).

#### *2.2. Preparation of Adsorbent*

The iron-bearing sludge came from a chemical plant wastewater, treatment sedimentation pond in Anqing City. Firstly, the iron-containing coagulant sludge was placed in an oven at 80 ◦C before being ground into a powder. The weighted amount of dried composites was calcined in a tubular furnace, heated to 400 ◦C, 500 ◦C, and 600 ◦C at a

rate of 2.4 ◦C/min under nitrogen purging, and kept at these conditions for three hours (FCN-x x = 0, 200, 400, 500, and 600 ◦C). Then the FCN-x composites were cooled to ambient temperature in the furnace and used in the experiments given below.

#### *2.3. Materials Characterization and Other Elemental Analysis*

An X-ray diffractometer (PANalytical X'Pert Pro, Almelo, The Netherlands) was used to analyze the crystalline structure of the FCN-x composites using 2θ = 5◦ to 80◦ scan range (40 kV and 30 mA applied current). Thermal field emission scanning electron microscopy (FE-SEM; SU-8020-Hitachi, Tokyo, Japan) and energy dispersive X-ray spectrometer (EDS) were used to study the morphology, microstructure, and elemental composition of the FCNx composites. X-ray photoelectron spectroscopy (XPS; model number Shimadzu, Kyoto, Japan) was used to determine the atomic composition and valence states of Fe, C, N, O, and Cr of the samples after Cr(VI) adsorption by FCN-500. All XPS data analyses were carried out with dedicated data fitting code for processing code (Peak Fit V4.12, Systat Software, Inc., Chicago, IL, USA). The FCN-x composites were examined under Fourier transform infrared spectrometer to probe surface reactivities (FT-IR; Thermo Nicolet, Waltham, MA, USA). The thermogravimetric analysis (TGA) curve of sludge was collected on a TGA8000 analyzer in high purity nitrogen from PE. The BET-specific surface area measurements of FCN-x were carried out by Brunauer-Emmett-Teller (BET; Autosorb—IQ3, Houston, TX, USA) method under liquid nitrogen temperature. The magnetic properties of the FCN-x composites were measured by a vibrating sample magnetometer (VSM; 7307, Lake Shore Cryotronics, Inc., Westerville, OH, USA). The content of Cr(VI) in solution was analyzed by laser-plasma mass spectrometry (LA-ICP-MS, Coherent Inc., Santa Clara, CA, USA). An X-ray fluorescence spectrometer (XRF; model number Shimadzu, Kyoto, Japan) was used to determine the chemical composition of the FCN-x.

#### *2.4. Batch Adsorption Experiments*

A series of batch experiments were carried out to determine the efficiency of FCN-x and the effects of pH, contact time, and adsorbate/adsorbent loading on Cr(VI) adsorption by FCN-x. A typical batch solution was prepared as outlined below: A 0.04 g FCN-x (0.4 g/L FCN-x) was mixed with 50 mL of a 20 mg/L Cr(VI) solution, and the final volume was adjusted to 100 mL with distilled water. The effect of Cr(VI) adsorption by FCN-x as a function of solution pH (pH 2 to 11), equilibration time (t 10 to 120 min), adsorbate dosage (0.2 to 0.5 g/L), and adsorbent loading (10 mg/L to 50 mg/L) was examined using the batch solution. At the end of a specific time of the reaction, the suspensions were filtered using 0.45 μm membrane filters, and the supernatant Cr(VI) concentration was determined UV spectroscopically at 540 nm (Cary-5000, Agilent, Santa Clara, CA, USA). The adsorption efficiency (H%) and amount of adsorbate are calculated as:

$$\mathrm{H} = \frac{\left(\mathrm{c\_0} - \mathrm{c\_t}\right)}{\mathrm{c\_0}} \times 100\% \tag{1}$$

$$\mathbf{q}\_{\rm t} = \frac{(\mathbf{c}\_0 - \mathbf{c}\_l) \times \mathbf{v}}{\mathbf{m}} \tag{2}$$

where c0 (mg/L) and ct (mg/L) are the concentrations of Cr(VI) before and after the reaction, respectively.

To investigate the mechanism of the adsorption/desorption processes, kinetic adsorption experiments were performed with 40 mg/L of Cr(VI) at 35 ◦C. Samples were taken at 20 min intervals to determine the adsorption capacity.

#### *2.5. Adsorption Regeneration Experiments*

The iron-based carbon material in the recovered solution was mixed with an excess of 1 M NaOH and stirred for 24 h, then filtered and adjusted to pH~7.00 with the distilled water. Then the substrate was dried in an oven at 80 ◦C for 4 h. The dried material was used to carry out the next Cr(VI) adsorption experiment, as detailed in Section 2.4. The same process was repeated five times.

#### *2.6. Treatment of Wastewater in the Electroplating Industry*

The wastewater received from an electroplating wastewater plant was filtered to remove suspended solids. The pH of the waste solution is then adjusted to about 2–3. The Cr(VI) adsorption experiment at a specific pH value was carried out, as discussed in Section 2.4.

#### **3. Results and Discussion**

#### *3.1. Characteristics of the Materials*

Figure 1 shows the scanning electron microscopic images (SEM) of the FCN-0 and the FCN-500 composites. The two materials are morphologically flocculent aggregates with non-uniform particle sizes. The pores of the FCN-0 are small in number and large in size. The FCN-500 shown in Figure 1b is tightly aggregated and clustered; the number of pores is significantly increased, and the particle size is smaller. The EDS data (Figure 1c) confirms the four elements of C, Fe, N, and O with a weight ratio of 2.5:7.7:0.4:2.5 (right top inset of Figure 1c) are uniformly distributed on the FCN-500. The Fe:O weight ratio of the FCN-500 (2.264) is higher than that of the FCN-0 (2.125, Figure S1), which is mainly caused by the decomposition of crystal water and some organic components during the calcination process.

**Figure 1.** SEM images of the FCN-0 (**a**), FCN-500 (**b**), and elemental mapping images (**c**) of the FCN-500.

Figure 2a shows the X-ray diffraction patterns of the FCN-0, FCN-400, FCN-500, and FCN-600. The diffraction peaks of FCN-0 at 2θ = 28.52◦, 36.38◦, and 52.81◦ correspond to the (040), (031), and (151) planes of FeOOH (JCPDS NO.74-1877), respectively. The peaks of FCN-400 at 2θ = 33.2◦, 49.5◦, and 54.1◦ can be attributed to the (104), (024), and (116) planes

of Fe2O3 (JCPDS 79-0007), respectively. While the other three diffraction peaks at 2θ = 36.1◦, 41.9◦, and 60.7◦ corresponding to the (111), (200), and (220) planes of FeO (JCPDS 77-2355), respectively. These results indicate that as the temperature rises, the FeOOH in the raw sludge dehydrates to form Fe2O3, then reduced to FeO by the carbon in the sludge. The intense peaks of FCN-500 at 2θ = 30.2◦, 35.5◦, 43.1◦, 53.5◦, 57.1◦, and 62.6◦ correspond to the (220), (311), (400), (422), (511), and (440) planes of Fe3O4 (JCPDS NO.19-0629), respectively. Therefore, it can be speculated that the sludge gradually agglomerated to form a Fe3O4 nanoparticle structure during the calcination process [33].

**Figure 2.** XRD patterns (**a**), FT-IR spectra (**b**) of the FCN-0, FCN-400, FCN-500, and FCN-600.

Figure 2b shows the FT-IR spectra of the FCN-0, FCN-400, FCN-500, and FCN-600. The broad band of FCN-0 in the range of 3110–3340 cm−<sup>1</sup> is attributed to the stretching vibration of O-H, which is caused by the adsorption of water molecules on the sludge surface. However, the O-H absorption band in FCN-400, FCN-500, and FCN-600 became flat, indicating that the pyrolysis process led to the disappearance of crystal water in the sludge. The two bands at 1600 cm−<sup>1</sup> and 1540 cm−<sup>1</sup> belong to the C=C stretching vibrations, and the IR band at 890 cm−<sup>1</sup> confirms the aromatic ring structure in the FCN-0 composite [34–36]. The wide absorption band at 1110 cm−<sup>1</sup> indicates the presence of a C-OH bond. The band at 554 cm−<sup>1</sup> of FCN-0 originated from the vibration of Fe-O, which transformed into the Fe-O-Fe bond (476 cm−<sup>1</sup> of FCN-500) after calcination at 500 ◦C, suggesting that 500 ◦C is the optimal temperature for the crystallization of iron oxides.

Figure 3 shows the magnetization curves of the FCN-0, FCN-400, FCN-500, and FCN-600 composites. The FCN-500 and FCN-600 exhibit typical ferromagnetic behavior, and the highest saturation magnetization value of the FCN-500 is about 54.3 emu/g. This is mainly because the organic components in the sludge are carbonized at high temperatures, and the Fe-containing substances in it are converted into magnetic Fe3O4. The high saturated magnetization of the FCN-500 facilitates its separation from the solution under an external magnetic field.

The thermal stability and composition of iron-containing coagulated sludge were studied in the temperature range of 30–1000 ◦C (Figure 4). The DTG curve showed three obvious weight loss stages during the heating process. The first stage occurred in the temperature range of 30–150 ◦C, which was mainly due to the loss of crystal water. In the second stage, in the temperature range of 150–350 ◦C, the weight decreased rapidly, mainly due to the thermal decomposition of organic matter containing nitrogen, sulfur, oxygen, and other elements in the coagulated sludge. The third stage of weight loss occurred at 350–500 ◦C, during which organics were further carbonized. Combining the XRD results and the magnetization curves, it can be inferred that FeOOH in iron-containing coagulated sludge was dehydrated and transformed into Fe2O3 in the temperature range of 30–350 ◦C. When the

temperature was higher than 350 ◦C, Fe2O3 was reduced to FeO by C in the sludge, and gradually converted to Fe3O4.

**Figure 3.** The magnetization curves of the FCN-0, FCN-400, FCN-500, and FCN-600.

**Figure 4.** DTG curve of the coagulated sludge.

The N2 adsorption-desorption isotherms of the FCN-0, FCN-400, FCN-500, and FCN-600 are used to estimate specific surface area and pore size distribution, as displayed in Figure S2. Table S1 shows the pore structure parameters (specific surface area, pore volume, and average pore diameter) of the FCN-0, FCN-400, FCN-500, and FCN-600. It can be seen that calcination has little effect on the specific surface area and pore size of materials.

Table S2 shows the chemical properties of the FCN-0, FCN-400, FCN-500, and FCN-600. According to the XRF results, as shown in the table below, the major metal element in the FCN-x is iron, which is approximately 70%, and the content of each other metal element is less than 1%.

#### *3.2. Parameters Optimization for Cr(VI) Adsorption*

The parameters, viz., composite type, pH, reaction time, temperature, adsorbate, and adsorbent loading on Cr(VI) adsorption, were optimized. The removal of Cr(VI) by different composites was first investigated. As shown in Figures 5a and S3a, after 2 h of reaction, the adsorption capacities of the FCN-0, FCN-400, FCN-500 and FCN-600 for Cr(VI) were 2.5 mg/g, 15 mg/g, 37.4 mg/g and 37.5 mg/g, respectively. With the increase in pyrolysis temperature, the adsorption performance of the prepared materials gradually improved. Combined with the results of magnetization curve analysis, FCN-500 showed the most efficient Cr(VI) adsorption capacity and good magnetic separation. This happened because the complex organic matter in the sludge was not completely decomposed at lower pyrolysis temperatures, while at a higher temperature, some functional groups on the sludge surface disappeared, thereby reducing the adsorption effect of Cr(VI), which was consistent with the FT-IR characterization results. Therefore, FCN-500 was selected for further research.

**Figure 5.** The removal of Cr(VI) by different adsorbents. (**a**) Effect of different adsorbents (T = 35 ◦C, pH = 2, [Cr(VI)] = 30 mg/L, [Adsorbents] = 0.8 g/L). (**b**) Effect of the pH value (T = 35 ◦C, [Cr(VI)] = 30 mg/L, [FCN-500] = 0.8 g/L). (**c**) Effect of the temperature (pH = 2, [Cr(VI)] = 30 mg/L, [FCN-500] = 0.8 g/L). (**d**) Effect of the initial Cr(VI) concentration (T = 35 ◦C, pH = 2, [FCN-500] = 0.8 g/L).

The pH of the solution affects the adsorption of Cr(VI) mainly by changing the surface charge and the degree of ionization of the material, as well as the morphology of Cr(VI) in solution [37]. The adsorption of Cr(VI) by FCN-500 was investigated when the pH value changed from 1.0 to 10.0. It can be observed from Figures 5b and S3b that the adsorption of Cr(VI) reached its best effect at pH = 2, and the removal of Cr(VI) was 99.9%. When the pH value increased, the removal of Cr(VI) started to decrease. The adsorption amount of Cr(VI) by the FCN-500 after 2 h was 20.4 mg/g when the solution pH was 7, and the removal rate dropped from 99.9% to 52%; further increased the pH to 8–10, the adsorption

of Cr(VI) dropped sharply to 5 mg/g, and the removal rate decreased to 12%. We supposed the reason for the change was as follows: Cr(VI) in solution mainly existed in the form of HCrO4 − and Cr2O7 <sup>2</sup><sup>−</sup> at low pH, while the nitrogen-containing species contained on the surface of the FCN-500 would be protonated under acidic conditions and converted to positively charged nitrogen, thereby adsorbing negatively charged HCrO4 − and Cr2O7 2− through electrostatic attraction and reducing Cr(VI) to Cr(III) by a redox reaction. With the pH increasing, the high concentration of OH− ions would compete with Cr(VI), resulting in a decrease in the adsorption performance of the material. In addition, when the pH was 1, Cr(VI) mainly existed in the form of the H2CrO4 compound, which was not conducive to the adsorption of the material [38,39].

The effect of temperature on Cr(VI) adsorption by FCN-500 was studied and shown in Figures 5c and S3c. The removal of Cr(VI) was 76%, 89.5%, and 99.9% when the reaction was carried out at 25 ◦C 30 ◦C, and 35 ◦C, respectively. The increase in reaction temperature is beneficial for accelerating the diffusion rate of pollutant separation, resulting in a higher adsorption capacity and the removal of Cr(VI).

The adsorption performance of FCN-500 at different initial concentrations of Cr(VI) was investigated, as shown in Figures 5d and S3d. When the initial concentration of Cr(VI) was 20 mg/L, the adsorption amount of FCN-500 was 25 mg/g at 2 h of reaction, and the removal rate was 100%. When the concentration of Cr(VI) increased to 30 and 40 mg/L, the adsorption capacity of FCN-500 also increased to 37.4 mg/g and 44.5 mg/g, respectively. However, the removal rate began to decrease gradually. When the initial concentration of Cr(VI) is low, there are many adsorption sites in FCN-500 that can adsorb and bind Cr(VI) in a large amount; when the concentration of pollutants is too high, the material is close to adsorption saturation, and the adsorption process tends to be stable [40].

#### *3.3. Adsorption Isotherms*

The adsorption isotherms are used to describe the distributed adsorbed molecules between the liquid and solid phases under equilibrium conditions [41]. The Langmuir model and Freundlich model were adopted to analyze the adsorption performance of 0.8 g/L FCN-500 at a reaction temperature of 35 ◦C, solution pH = 2.0. The two model equations are shown as follows:

Langmuir model equation:

$$\frac{\mathbf{c\_e}}{\mathbf{q\_e}} = \frac{1}{\mathbf{q\_m}\mathbf{b}} + \frac{\mathbf{c\_e}}{\mathbf{q\_m}} \tag{3}$$

Freundlich model equation:

$$\text{lnq}\_{\text{e}} = \text{lnK}\_{\text{f}} + \frac{1}{\text{n}} \text{lnc}\_{\text{e}} \tag{4}$$

where Ce (mg/L) is the concentration of Cr(VI) in solution at the adsorption equilibrium; qm (mg/g) is the maximum adsorption capacity; b is the Langmuir constant; Kf and n are the Freundlich constants.

The influence of the initial concentration of Cr(VI) on the equilibrium amount of Cr(VI) adsorbed by FCN-500 was evaluated under the same experimental conditions. The isotherms of Cr(VI) adsorption by FCN-500 were further fitted by Langmuir and Freundlich models, and the results are shown in Figure S4. The correlation coefficient (R2) of the Langmuir model and Freundlich model was 0.993 and 0.976, respectively. Therefore, the Langmuir model was most suitable for describing Cr(VI) adsorption by FCN-500. The Cr(VI) adsorption capacity with various allied substrates with the FCN 500 is shown in Table 1. The initial conditions, such as pH value, adsorbate, and adsorbent loading, were different. The FCN-500 showed the highest normalized Cr(VI) adsorption compared to other substrates.


**Table 1.** Comparison of Cr(VI) adsorption capacity using FCN-500 with other reported adsorbents.

#### *3.4. XPS Spectra Analysis*

XPS is a characterization analysis method to confirm Cr(VI) reduction on FCN-500 by measuring element species. As shown in Figure 6 of the scan spectra, the distinct peaks observed correspond to Fe 2p, C 1s, N 1s, O 1s, and Cr 2p. Cr(VI) adsorption on FCN-500 is confirmed by the presence of the Cr 2p peak (Figure 6a). In Cr 2p, the peaks at 576.6 and 586.5 eV were marked as Cr 2p3/2 and Cr 2p1/2 of Cr(III) (Figure 6b) [46,47]. The presence of Cr(III) indicates subsequent Cr(VI) reduction. The Fe2p peak is mainly divided into Fe2p3/2 and Fe2p1/2, which were located at 711.7 eV and 724.6 eV, respectively (Figure 6c). The Fe 2p3/2 peak is located at 711.7 eV, indicating Fe3O4 in FCN-500, which is consistent with the results of the XRD analysis [48]. The peak corresponding to the C 1s peak shows indifferent behavior in all samples. In the binding spectrum of C1s, the peaks at 284.8 eV, 285.8 eV and 288.3 eV correspond to the C-C/CHx, -C-OR and N-C=N bonds, respectively (Figure 6d) [49,50]. In the O 1s peak (Figure 6e), before Cr(VI) adsorption, O1s has three XPS peaks with binding energies at 530.2 eV, 531.9 eV, and 532.8 eV, which are assigned to Fe-O, C-O, and C=O groups [51]. After Cr(VI) adsorption, the O1s spectrum has fitted into three peaks with the binding energies at 530.2 eV, 531.5 eV, and 532.5 eV, which are assigned to Fe-O, C-O, and C=O groups. After adsorption, the peak intensity of C=O is decreased. The results showed that some of the C=O in FCN-500 had been oxidized to C-O after the adsorption of Cr(VI), indicating that a redox reaction occurred during the adsorption process [27]. The high-resolution spectrum at N 1s shows three peaks at 398.7, 399.8, and 400.7 eV, attributed to =N–, –NH–, and N<sup>+</sup> (=N–<sup>+</sup> and –NH–+), respectively. After Cr(VI) adsorption, the N<sup>+</sup> content increased significantly, whereas the total amount of = N– and –NH– decreased. The results show that part of nitrogen on FCN-500 is further protonated under acidic conditions, and the non-protonated nitrogen is significantly reduced (Figure 6f) [27].

#### *3.5. Adsorption Mechanism*

As shown in half cell reactions (5) and (6), when the pH value of the solution increases from 0 to 14, the oxidation potential of Cr(VI) decreases from 1.33 V to −0.13 V [52]. A strongly acidic environment is conducive to the Cr(VI) species in solution. In the present study, we used pH 2 to endow Cr(VI) in solution.

$$\rm{Cr\_2O\_7}^{2-} + 6e^- + 14H^+ \to 2Cr^{3+} + 7H\_2O, E^0 = 1.33 V \ (pH = 0) \tag{5}$$

$$\text{CrO}\_4\text{}^{2-} + 4\text{H}\_2\text{O} + 3\text{e}^- \rightarrow \text{Cr}^{3+} + 8\text{OH}^-\text{,} \text{E}^0 = -0.13\text{ V (pH} = 14) \tag{6}$$

The reduction product was confirmed to be Cr(III) by XPS analysis. ICP-MS analysis of chromium species noted a negligible proportion of Cr(III) in solution after adsorptionreduction, indicating that the adsorbed Cr(III) was not released into the solution, but was instead immobilized on the surface of the FCN-500. The nitrogen atoms in the PDA play a key role in Cr(III) chelation [53]. However, protonated nitrogen atoms are not suitable for chelating Cr(III) due to electrostatic repulsion. Therefore, non-protonated nitrogen atoms may be the main reason for the fixation of Cr(III) after the redox reaction [27].

In summary, the reduction reaction is accompanied by the adsorption process, and the reducing agent is the nitrogen atomic group on the FCN-500. After reduction, the produced Cr(III) was immobilized on the adsorbent. It should be noted that the adsorption mechanism for Cr(VI) involves several steps. (1) The adsorption of Cr(VI) occurred in an aqueous solution, resulting in the protonated nitrogen groups of the active site on the FCN-500 at pH = 2; (2) The adsorption of Cr(VI) on the active sites was reduced to Cr(III) by nitrogen groups on the FCN-500; (3) The reduced Cr(III) was immobilized in situ on the adsorbent surface by chelating with aprotic nitrogen atoms.

**Figure 6.** XPS spectra of FCN-500 before and after Cr(VI) removal: wide scan (**a**) and high-resolution spectra of (**b**) Cr2p, (**c**) Fe2p, (**d**) C1s, (**e**) O1s and (**f**) N1s.

#### *3.6. Adsorption Regeneration Experiments*

After adsorption-reduction, chromium occurs on FCN-500 as Cr(III). The regeneration of reactive sites was carried out using 1 M NaOH for 12 h (each cycle). After five adsorptiondesorption cycles (Figure 7a), the adsorption capacity of FCN-500 decreased slightly, reaching about 80% optimal capacity. As the number of regenerations increased, the adsorption capacity declined after the third regeneration step. The adsorption capacity of the regenerated FCN-500 remained unchanged after five cycles. The XRD patterns of fresh and used FCN-500 were also collected as shown in Figure 7b. Results suggested that after five cycles of adsorption-regeneration, the basic structure of FCN-500 was well maintained, proving its good regenerative adsorption performance of FCN-500. In addition, a new peak near 42◦ was observed for the used FCN-500, which can be associated to the crystal plane of

some Cr-N-Fe species, indicating that Cr interacted with nitrogen groups on FCN-500 [54]. This result also proved the proposed mechanism, that nitrogen-containing groups were the active sites for Cr adsorption.

**Figure 7.** (**a**) Experimental diagram of adsorption and regeneration. (**b**) XRD patterns of fresh and used FCN-500.

#### *3.7. Treatment of Wastewater in the Electroplating Industry*

Electroplating wastewater discharged from electroplating production contains a large amount of chromium, heavy metals, and organic matter. The pH of electroplating wastewater is around 2–3, so the FCN-500 can be used to remove Cr(VI) without further adjustment. As shown in Figure S5, the removal of total chromium by FCN-500 reached 99.93% after 2 h reaction under the conditions of T = 25 ◦C, pH = 3, [Cr] = 117 mg/L, and [FCN-500] = 2.4 g/L. The results showed that the FCN-500 magnetic material also had a good adsorption effect on Cr in the actual wastewater.

#### **4. Conclusions**

In this study, the magnetic Fe-C-N composite was successfully prepared from the coagulation sludge produced from dye chemical wastewater as a starting material to develop a Cr(VI) removal substrate designated as FCN. The material obtained when the pyrolysis temperature was 500 ◦C (FCN-500) performed the best adsorption performance for Cr(VI). The maximum adsorption capacity for Cr(VI) by FCN-500 was 52.63 mg/g at 35 ◦C and pH = 2. The Cr(VI) removal from the solution by FCN 500 occurs due to the adsorptive–reduction process. The Cr(VI) → Cr(III) reduction preferentially occurred at hetero N sites, followed by Cr(III) adsorption on aprotic N on FCN-500. Besides, FCN-500 also showed good reusability, with approximately 80% removal of Cr(VI) after 5 cycles. This work provides a new method for recycling chemical solid waste sustainably as a value-added product.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w15122290/s1.

**Author Contributions:** Conceptualization, X.C.; Methodology, X.L., H.L., Z.D. and B.W.; Formal analysis, X.L.; Investigation, X.L., H.L., Z.D. and B.W.; Writing—original draft, X.L. and H.L.; Writing—review & editing, R.W. and X.C.; Supervision, K.C. and X.C.; Funding acquisition, K.C. and X.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors acknowledge the financial support from the National Key R&D Program of China (2019YFC0408500), and the Key Science and Technology Projects of Anhui Province (202003a07020004).

**Data Availability Statement:** Not applicable.

**Acknowledgments:** R.W. acknowledges the Program of Distinguished Professor in B&R Countries (Grant No. G20200012010).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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