# Adsorption of Copper and Lead Ions in a Binary System onto Orange Peels: Optimization, Equilibrium, and Kinetic Study

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

^{2}) of 0.973 and 0.993 for Cu and Pb, respectively. The bio-sorption of Cu and Pb increased with increasing adsorbent dosage while the percentage removal of Pb was consistently higher than Cu. The highest percentage removal of Cu and Pb gave 86.27% and 98.85%, respectively. The kinetic and isotherm studies showed that pseudo-second-order and Langmuir isotherm models fitted the experimental data suggesting chemisorption and monolayer adsorption, respectively. The treatment of wastewater is very essential to avoid water scarcity and to achieve the Sustainable Development Goals (SDGs). This study demonstrates the potential of utilizing orange peels as bio-sorbent for the treatment of wastewater containing Cu and Pb ions.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Preparation of Bio-Sorbent and Characterization

#### 2.2. Preparation of the Synthetic Solution

_{3})

_{2}.3H

_{2}O and 1.6 g PbNO

_{3}in distilled water. The chemicals were of analytical grade and purchased from Laboratory Analytical Supplies Limited, South Africa. The desired concentration needed for each experimental run was obtained from the prepared stock solution by dilution. A digital pH meter (edge pH HI 2002) was used to determine the pH value of the solution while 0.1 M H

_{2}SO

_{4}or 0.1 M NaOH was added in drops to obtain the desired pH value.

#### 2.3. Batch Adsorption Studies

#### 2.4. Bio-Sorption Equilibrium

#### 2.4.1. Langmuir Isotherm

#### 2.4.2. Freundlich Isotherm

#### 2.5. Adsorption Kinetic Studies

^{−1}) and pseudo-second-order (g mg

^{−1}min

^{−1}) rate constant, respectively, and t is the contact time (min).

#### 2.6. Central Composite Design (CCD)

^{2+}and Pb

^{2+}ions in binary solute system onto orange peels are initial metal ion concentration, adsorbent dosage, and particle size at specified factor levels (Table 1). The CCD generated a total of 20 experimental runs with six replicates using a face-centered design. In the optimization study, the second-order polynomial equation was used to explain the interactive effects of the independent variables. The quadratic model used to optimize the process variables is shown below.

_{i}and X

_{j}are the independent variables; β

_{o}, β

_{i}, β

_{ii}, and β

_{ij}are the regression coefficients; and Ɛ is the residual error. The interpretation of the experimental results and the significant variables are explained using mathematical functions called the analysis of variance (ANOVA).

## 3. Results and Discussion

#### 3.1. Point of Zero Charges of Orange Peels (pHpzc)

#### 3.2. Characterization of Bio-Sorbent

#### 3.2.1. FTIR Spectroscopy Analysis

^{−1}wavenumber.

^{−1}, which indicates the presence of an intermolecular bonded O–H group consisting of alcohols and phenols. The peaks at 2918.92 cm

^{−1}, 2850.97 cm

^{−1}, 1441.94 cm

^{−1}, 1374.74 cm

^{−1}, and 887.26 cm

^{−1}indicate the presence of a saturated C–H substitution bond, which can be likened to alkanes that are available in the pulp. The peaks at 1734.38 cm

^{−1}and 1607.04 cm

^{−1}reveal the presence of the carboxylic C=O bond and the unsaturated C=C bond, respectively, which are attributed to aldehydes and ketones. The existence of a high quantity of hydroxyl and carboxyl groups on the surface of the bio-sorbent suggests the tendency of orange peels to adsorb positively charged metal ions. The presence of these functional groups plays an important role in the bio-sorption of heavy metals through ion exchange, which occurs as a result of the affinity of metal ions to the functional groups.

#### 3.2.2. Scanning Electron Microscope-Energy Dispersive X-ray (SEM-EDX)

^{2+}ions than Cu

^{2+}ions. The surface area of an adsorbent also plays an important role in an adsorption process. The BET surface area of orange peel bio-sorbent relative to the particle size 75, 255, and 455 µm gave 9.8471, 6.6383, and 3.5685 m

^{2}/g, respectively.

#### 3.3. Experimental Design

#### 3.3.1. Adsorption of Cu^{2+} and Pb^{2+} onto Orange Peels in Binary Solute Using CCD

^{2+}and Pb

^{2+}ions onto orange peels is presented in Table 2 with the predicted and experimental responses. The second-order polynomial Equation (6) with multiple regression analysis was used to generate the responses (percentage removal of Cu

^{2+}and Pb

^{2+}) using the three design factors.

_{1}) and percentage removal of Cu (Y

_{2}) written for coded factors as a function of initial concentration (A), adsorbent dosage (B), and the particle size (C) are represented in Equations (9) and (10), respectively.

^{2}, B

^{2}, C

^{2}). The order of the interactive factors to increase the percentage removal of Pb(II) is AC > AB > BC while for percentage removal of Cu(II) is BC > AC > AB.

#### 3.3.2. Analysis of Variance (ANOVA) for the Models

^{2+}and Cu

^{2+}gave <0.0001, which implies that the regression model equation fitted well with the experimental data. The lack of fit value greater than 0.05 signifies that the models have one or more terms that have no significant influence on the regression models because of pure error or noise. However, such terms were included in the model to justify the parent terms and the interactive behavior. In the case of percentage removal of Pb

^{2+}, the significant model parameters are initial concentration A, adsorbent dosage B, interactive terms (AB, AC, BC), and quadratic term C

^{2}while particle size C and quadratic terms (A

^{2}and B

^{2}) are not significant. The significant model parameters for the percentage removal of Cu

^{2+}are adsorbent dosage B and particle size C, which have a first-order main effect, interactive terms (AB, AC, BC), and quadratic term B

^{2}while initial concentration A and quadratic terms (A

^{2}, and C

^{2}) are not significant.

^{2}), the adjusted R

^{2}, and the predicted R

^{2}. The R

^{2}helps to ascertain the closeness of the experimental values to the predicted values, which range from 0 to 1, where 0 denotes no correlation between the data. The R

^{2}obtained for the regression models was found to be close to 1, which implies a good fit between the experimental and the predicted data as represented in Figure 5. The predicted R

^{2}and the adjusted R

^{2}for the regression models of Pb

^{2+}and Cu

^{2+}are in reasonable agreement with a difference of less than 0.2, which validates the significance of the models. The R

^{2}values for the models were found to be 0.9927 and 0.9725 for percentage Pb

^{2+}removal and percentage Cu

^{2+}removal, respectively. The adequate precision of the models, which is a measure of the signal-to-noise ratio, was desirable for a ratio greater than 4.

#### 3.3.3. D Representation of the Interactive Effects on the Responses

^{2+}and Cu

^{2+}ions. The bio-sorption capacity of Pb

^{2+}increased with increasing adsorbent dosage while an increase in the initial concentration had slight changes in the adsorption capacity. This suggests that the surface of the bio-sorbent has reached the saturation point. In the case of the percentage removal of Cu

^{2+}, the initial concentration and the adsorbent dosage increased with increasing percentage removal. The percentage removal of Pb

^{2+}and Cu

^{2+}increased with the increasing dosage of orange peels. This result is reasonable because a little amount of bio-sorbent relates to a small active site, and since the surface of the bio-sorbent is acidic, more active sites must be occupied for proton-metal ion competition.

^{2+}and Cu

^{2+}. The highest bio-sorption capacity of Pb

^{2+}and Cu

^{2+}gave 98.85% and 87.32%, respectively, at the initial concentration of 100 mg/L, while the particle size had no significant changes in the adsorption of Pb

^{2+}. On the contrary, the percentage removal of Cu

^{2+}increased with increasing initial concentration and particle size.

^{2+}and Cu

^{2+}. The bio-sorption capacity of Pb

^{2+}and Cu

^{2+}ions increased with increasing adsorbent dosage while the particle size had little effect on the adsorption efficiency of the metal ions. The intersection of the two graphs shows that the maximum percentage removal of Pb

^{2+}and Cu

^{2+}ions in the binary system was reached with a particle size of 75 microns. This result is significant because smaller particle sizes have a larger surface area and enhance adsorption capacity better than bigger particle sizes. Furthermore, Figure 6a–c showed that the percentage removal of Pb

^{2+}was higher than Cu

^{2+}with all the interactions, which are a result of the reactivity of Pb

^{2+}over Cu

^{2+}[15]. Hence, Pb

^{2+}ions were more adsorbed in the binary system than Cu

^{2+}ions, as represented in Table 2. In conclusion, adsorbent dosage had the highest influence on the percentage removal of Pb

^{2+}and Cu

^{2+}ions in the binary system followed by the initial concentration while particle size had little or no significant effect.

#### 3.3.4. Optimization of the Adsorption Process

^{2+}and Pb

^{2+}ions in the binary system using orange peels gave 98.85% and 86.27% removal for Pb

^{2+}and Cu

^{2+}ions, respectively, with an initial concentration of 100 mg/L, an adsorbent dosage of 1 g, and particle size of 75 µm. The desirability of the solution gave 0.895, which signifies that the optimum condition is reasonably acceptable.

#### 3.3.5. Mechanism of Adsorption of Cu^{2+} and Pb^{2+} Ions

^{2+}and Pb

^{2+}ions onto orange peels was achieved by the combination of the operating parameters. The functional groups identified on the surface of orange peels played a significant role in the bio-sorption process. The major shifts in the peaks representing –COOH and –OH after adsorption of Cu

^{2+}and Pb

^{2+}confirmed ion exchange as the adsorption mechanism. These functional groups become deprotonated at a solution pH higher than the pHpzc of the bio-sorbent, which favors adsorption uptake of cations. In addition, the displacement of the metals present on the surface of orange peels after adsorption (Figure 3) also revealed ion exchange as the adsorption mechanism responsible for the bio-sorption of Cu

^{2+}and Pb

^{2+}in a binary system using orange peels.

#### 3.4. Equilibrium Study

^{2+}and Pb

^{2+}onto orange peels were fitted with Langmuir and Freundlich isotherm models. A very important guide to determining the best isotherm is to fit the experimental data with different isotherm models for estimation, and then compare the correlation coefficient (R

^{2}) values obtained [16].

^{2+}and Cu

^{2+}using orange peels is presented in Table 5.

^{2+}and Pb

^{2+}in the binary system as well as the linear fits for the Langmuir isotherm model. The adsorption of Cu

^{2+}and Pb

^{2+}increased with increasing initial metal ion concentrations. The Langmuir isotherm constant “b” is higher for Pb

^{2+}(0.77 L/mg) than Cu

^{2+}(0.15 L/mg), which buttresses the fact that Pb

^{2+}was more adsorbed than Cu

^{2+}. In addition, the quantity adsorbed of Pb

^{2+}was higher than Cu

^{2+}. Therefore, Pb

^{2+}has a higher affinity for the active sites on the surface of orange peels. Comparing the correlation coefficient R

^{2}value for Langmuir and Freundlich isotherm models (Table 5), it is obvious that the Langmuir isotherm fitted the experimental data well for both Cu

^{2+}and Pb

^{2+}with values of R

^{2}very close to 1. Another important characteristic of the Langmuir isotherm model can be represented by a dimensionless factor R

_{L}, also known as the separation factor. This is used to determine the adsorption behavior of the bio-sorbent as presented in Table 5. The dimensionless factor, R

_{L}, for the adsorption of Pb

^{2+}and Cu

^{2+}in binary system onto orange peels falls within the range of 0 < R

_{L}< 1, which implies that the adsorption process is favorable within the concentration range studied. Hence, the orange peel is efficient for the adsorption of Pb

^{2+}and Cu

^{2+}ions from aqueous solutions.

^{2+}and Cu

^{2+}. The parameters K

_{F}and n are the Freundlich isotherm constants describing the adsorption capacity and intensity, respectively. The measure of the exponent ‘n’ explains the adsorbent–sorbent phenomenon capacity and favorability [19]. Table 5 shows the values of n and the interpretation. The Freundlich isotherm constant “n”, which evaluates the adsorption intensity of the metal ions was determined to be greater than 1 and in the range 1 < n < 10 for Pb

^{2+}and Cu

^{2+}in the binary system. This shows that the adsorption process is favorable and with high chemisorption. This implies there exist great bonds between the adsorbent and the adsorbate because of the chemisorption reactions.

#### 3.5. Kinetic Modelling of Pb^{2+} and Cu^{2+} onto Orange Peels

^{2+}and Cu

^{2+}onto orange peels in binary systems with the effect of time at different initial concentrations (10, 55, and 100 mg/L) was investigated using the linearized pseudo-first-order and pseudo-second-order model. The calculated constant parameters obtained from the pseudo-first-order and pseudo-second-order linear models using orange peels are presented in Table 6. The effect of time on different initial concentrations was studied in the binary system containing Cu

^{2+}and Pb

^{2+}. Figure 8a represents the plot of $\mathrm{log}\left({q}_{e}-{q}_{t}\right)$ against t for the binary system of Cu

^{2+}and Pb

^{2+}using orange peels. The correlation coefficient (R

^{2}) for the binary solute Cu

^{2+}and Pb

^{2+}pseudo-first-order model was in the range of 0.762 to 0.958. The pseudo-second-order model showed that the quantity of Pb

^{2+}adsorbed is higher than Cu

^{2+}for all the initial concentrations. The pseudo-second-order constant K

_{2}value for Pb

^{2+}was higher than Cu

^{2+}signifying that adsorption of Cu

^{2+}is negatively affected in the coexistence of Pb

^{2+}. The correlation coefficient for the pseudo-second order was observed to be very close to 1 for Cu

^{2+}and Pb

^{2+}at the different initial concentrations, which suggests that the adsorption rate is chemically controlled. Figure 8b shows the graph representing the linear form of the pseudo-second-order model of Cu(II) and Pb(II) in a binary system using orange peels. It can be observed that the graph started from the origin, indicating the good fit of the model.

#### 3.6. Comparison of Cu^{2+} and Pb^{2+} Adsorption Capacity Using Orange Peels with Other Bio-Sorbents

^{2+}and Pb

^{2+}ions in binary solution is 38.18 mg/g and 40.05 mg/g, respectively. The adsorption uptake of Pb

^{2+}was higher than Cu

^{2+}, which implies that orange peels have a higher affinity for Pb

^{2+}than Cu

^{2+}. The maximum uptake of the metal ions obtained in this study is compared with other results reported using bio-sorbents as presented in Table 7.

## 4. Conclusions

^{2+}and Pb

^{2+}in a binary system from an aqueous solution using orange peels as a low-cost bio-sorbent was experimentally investigated in a batch mode. The interactive effect of the operating parameters, such as initial metal concertation, adsorbent dosage, and particle size, was examined using CCD. The adsorption capacity of Pb

^{2+}was consistently higher than Cu

^{2+}in all the experimental runs. The percentage removal increased with an increasing adsorbent dosage, which is attributed to the increase in the active sites available on the surface of the bio-sorbent. The adsorption of Cu

^{2+}and Pb

^{2+}occurred at a pH higher than the pHpzc of orange peels, which is 3.85. This reveals that the adsorption of cation is favored at pH > pHpzc of the adsorbent. The EDX analysis also showed that potassium, magnesium, and calcium disappeared on the surface of the bio-sorbent after adsorption. Hence, ion exchange is confirmed as the adsorption mechanism for the bio-sorption of Cu

^{2+}and Pb

^{2+}.

^{2+}and Pb

^{2+}from wastewater in a bi-solute system.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**EDX spectra and elemental weight % table of orange peels (

**a**) before adsorption and (

**b**) after adsorption of Cu and Pb.

**Figure 6.**3D surface plot of percentage removal of Cu and Pb with the interaction of variables; (

**a**) initial concentration against adsorbent dosage (

**b**) initial concentration against particle size (

**c**) adsorbent dosage against particle size.

**Figure 7.**Adsorption isotherm of Cu and Pb onto orange peels in the binary system at pH 5. The symbols are the experimental results while the solid lines are the linear fittings of (

**a**) Langmuir isotherm and (

**b**) Freundlich isotherm.

**Figure 8.**Kinetic model graph of binary Cu

^{2+}and Pb

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**a**) Pseudo-first-order model and (

**b**) pseudo-second-order model.

Parameters | Factor | Range and Level | ||
---|---|---|---|---|

−1 | 0 | +1 | ||

Initial concentration | X_{1} | 10 | 55 | 100 |

Adsorbent dosage | X_{2} | 0.1 | 0.55 | 1 |

Particle size | X_{3} | 75 | 265 | 455 |

Std | Run | Initial Conc. (mg/L) | Adsorbent Dosage (g) | Particle Size (µm) | Responses | |||
---|---|---|---|---|---|---|---|---|

Lead % Removal | Copper % Removal | |||||||

Exp | Pred | Exp | Pred | |||||

15 | 1 | 55 | 0.55 | 265 | 83.75 | 83.93 | 72.89 | 71.48 |

5 | 2 | 10 | 0.1 | 455 | 80.34 | 80.56 | 59.1 | 59.06 |

2 | 3 | 100 | 0.1 | 75 | 62.07 | 61.95 | 79.5 | 78.69 |

17 | 4 | 55 | 0.55 | 265 | 82.86 | 83.93 | 70.17 | 71.48 |

12 | 5 | 55 | 1 | 265 | 90.47 | 91.18 | 85.06 | 86.55 |

4 | 6 | 100 | 1 | 75 | 88.12 | 88.04 | 84.72 | 84.77 |

6 | 7 | 100 | 0.1 | 455 | 70.86 | 71.13 | 59.95 | 60.57 |

20 | 8 | 55 | 0.55 | 265 | 84.15 | 83.93 | 73.72 | 71.48 |

9 | 9 | 10 | 0.55 | 265 | 90.12 | 88.88 | 72.82 | 70.86 |

3 | 10 | 100 | 1 | 75 | 98.85 | 98.71 | 86.2 | 85.60 |

16 | 11 | 55 | 0.55 | 265 | 84.45 | 83.93 | 72.1 | 71.48 |

1 | 12 | 10 | 0.1 | 75 | 77.97 | 78.86 | 68.98 | 70.76 |

19 | 13 | 55 | 0.55 | 265 | 83.21 | 83.93 | 69.65 | 71.48 |

8 | 14 | 10 | 1 | 455 | 84.78 | 84.03 | 82.65 | 80.89 |

18 | 15 | 55 | 0.55 | 265 | 84.05 | 83.93 | 70.25 | 71.48 |

10 | 16 | 10 | 0.55 | 265 | 78.15 | 78.83 | 69.29 | 71.19 |

14 | 17 | 55 | 0.55 | 455 | 81.75 | 81.75 | 65.98 | 66.34 |

13 | 18 | 55 | 0.55 | 75 | 83.45 | 82.90 | 74.55 | 74.13 |

11 | 19 | 55 | 0.1 | 265 | 76.06 | 74.80 | 70.53 | 68.98 |

7 | 20 | 100 | 1 | 455 | 86.95 | 87.21 | 87.32 | 88.14 |

Source | Sum of Squares | df | Mean Square | F-Value | p-Value Prob > F | Comments |
---|---|---|---|---|---|---|

Pb Model | 1088.25 | 9 | 120.92 | 151.21 | <0.0001 | significant |

A-Initial concentration | 252.51 | 1 | 252.51 | 315.76 | <0.0001 | |

B-Adsorbent dosage | 670.27 | 1 | 670.27 | 838.17 | <0.0001 | |

C-Particle size | 3.34 | 1 | 3.34 | 4.18 | 0.0682 | |

AB | 19.47 | 1 | 19.47 | 24.35 | 0.0006 | |

AC | 28.05 | 1 | 28.05 | 35.08 | 0.0001 | |

BC | 87.12 | 1 | 87.12 | 108.94 | <0.0001 | |

A^{2} | 0.0138 | 1 | 0.0138 | 0.0173 | 0.8980 | |

B^{2} | 2.43 | 1 | 2.43 | 3.04 | 0.1116 | |

C^{2} | 7.09 | 1 | 7.09 | 8.87 | 0.0139 | |

Residual | 8.00 | 10 | 0.7997 | |||

Lack of Fit | 6.17 | 5 | 1.23 | 3.39 | 0.1034 | not significant |

Pure Error | 1.82 | 5 | 0.3647 | |||

Cor Total | 1096.25 | 19 | ||||

Std. Dev.0.8942 | R^{2} 0.9927 | AdjustedR^{2} 0.9861 | PredictedR^{2} 0.9483 | Adeq.Precision 58.15 | Mean 82.62 | C. V.%1.08 |

Source | Sum of Squares | df | Mean Square | F-Value | p-Value Prob > F | Comments |
---|---|---|---|---|---|---|

Cu Model | 1230.20 | 9 | 136.69 | 39.32 | <0.0001 | significant |

A-Initial concentration | 0.2856 | 1 | 0.2856 | 0.0822 | 0.7802 | |

B-Adsorbent dosage | 772.47 | 1 | 772.47 | 222.22 | <0.0001 | |

C-Particle size | 151.71 | 1 | 151.71 | 43.64 | <0.0001 | |

AB | 38.37 | 1 | 38.37 | 11.04 | 0.0077 | |

AC | 20.67 | 1 | 20.67 | 5.95 | 0.0349 | |

BC | 101.39 | 1 | 101.39 | 29.17 | 0.0003 | |

A^{2} | 0.5762 | 1 | 0.5762 | 0.1658 | 0.6925 | |

B^{2} | 108.53 | 1 | 108.53 | 31.22 | 0.0002 | |

C^{2} | 4.28 | 1 | 4.28 | 1.23 | 0.2931 | |

Residual | 34.76 | 10 | 3.48 | |||

Lack of Fit | 20.79 | 5 | 4.16 | 1.49 | 0.3365 | not significant |

Pure Error | 13.97 | 5 | 2.79 | |||

Cor Total | 1264.96 | 19 | ||||

Std. Dev.1.86 | R^{2} 0.9725 | AdjustedR^{2} 0.9478 | PredictedR^{2} 0.7934 | Adeq.Precision 22.06 | Mean 73.77 | C. V.%2.53 |

Ion | System | Langmuir | Freundlich | |||||
---|---|---|---|---|---|---|---|---|

b | q_{m} | R^{2} | R_{L} | K_{f} | n | R^{2} | ||

(L/mg) | (mg/g) | |||||||

Cu^{2+} | Binary | 0.15 | 38.18 | 0.988 | 0.4 | 5.19 | 1.56 | 0.934 |

Pb^{2+} | Binary | 0.77 | 40.05 | 0.998 | 0.02 | 24.06 | 4.29 | 0.898 |

**Table 6.**Pseudo-first-order model parameters for the adsorption of Cu

^{2+}and Pb

^{2+}using orange peels.

Metal Ion Concentration | Cu | Pb | |||||
---|---|---|---|---|---|---|---|

10 mg/L | 55 mg/L | 100 mg/L | 10 mg/L | 55 mg/L | 100 mg/L | ||

Pseudo-first order | Binary | ||||||

q_{e} | 6.83E6 | 465.27 | 2087.37 | 0.05 | 14.72 | 2.18 | |

K_{1} | 48.96 | 6.96 | 10.59 | 8.83 | 11.06 | 0.33 | |

R^{2} | 0.958 | 0.762 | 0.897 | 0.970 | 0.891 | 0.924 | |

Pseudo-second order | Binary | ||||||

q_{e} | 42.48 | 9.49 | 13.39 | 3.82 | 15.96 | 17.09 | |

K_{2} | 0.02 | 0.44 | 0.04 | 10.71 | 0.55 | 0.30 | |

R^{2} | 0.999 | 0.999 | 0.999 | 1.000 | 0.999 | 0.999 |

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**MDPI and ACS Style**

Afolabi, F.O.; Musonge, P.; Bakare, B.F.
Adsorption of Copper and Lead Ions in a Binary System onto Orange Peels: Optimization, Equilibrium, and Kinetic Study. *Sustainability* **2022**, *14*, 10860.
https://doi.org/10.3390/su141710860

**AMA Style**

Afolabi FO, Musonge P, Bakare BF.
Adsorption of Copper and Lead Ions in a Binary System onto Orange Peels: Optimization, Equilibrium, and Kinetic Study. *Sustainability*. 2022; 14(17):10860.
https://doi.org/10.3390/su141710860

**Chicago/Turabian Style**

Afolabi, Felicia Omolara, Paul Musonge, and Babatunde Femi Bakare.
2022. "Adsorption of Copper and Lead Ions in a Binary System onto Orange Peels: Optimization, Equilibrium, and Kinetic Study" *Sustainability* 14, no. 17: 10860.
https://doi.org/10.3390/su141710860