Sage Journals: Discover world-class research

Abstract

In this regard, green preparation of nanoscale-zero-valent copper was accomplished using Ficus benjamina leaves successfully. Ficus nano zero valent copper (Ficus-nZVCu) was characterized utilized scanning electron microscopy, and Fourier transform infrared spectroscopy. The results revealed that 63% of DR31 dye and 56% of DO26 dye were removed within the optimum conditions (0.3 g L^-1, 30 min, 50 mg L^-1, and pH 6). A Langmuir model was found to be the most fitting system for the removal process for DR31 and DO26. A Pseudo-second order kinetics model is the fit model of the removal processes for DR31 and DO26. The regenerated adsorbent still retained good adsorption capability after the fifth round of recycles. Ficus-nZVCu adsorbent is intensely recommended as a confident material for removing DR31 and DO26 anionic dyes from both prepared and actual wastewater samples. The co-presence of DR31 and DO26 did not affect each other's.

Keywords

Direct Red 31 Direct Orange 26 zero-valent Cu novel nanoparticles wastewater

Introduction

The development of prepared dyes has led to a chemical revolution (Pereira and Alves, 2012; Wu et al., 2021), resulting in a more colorful world. Synthetic dye has gradually replaced natural dye and dominated the dye industry due to advantages such as simple structure, superior coloration, non-fading properties, and wide application (Tkaczyk et al., 2020). As production scales and application fields have expanded, synthetic dyes have gradually permeated every aspect of life (Kümmerer, 2010). Synthetic dye has a vibrant color but has also caused severe water pollution. Dyes wastewater from the textile, paper, and other industries have become contributors to water pollution. The most popular organic dyes among different dye wastes are azo dyes. Azo compounds account for more than 50% of all dyes. Despite their widespread use, they are regarded as persistent, and recalcitrant in natural environments due to the precision in which their structures are designed to withstand optical and chemical processes (Selvaraj et al., 2021; Wang et al., 2022). Azo dyes are challenging to degrade under normal conditions because of their stable complex aromatic structures (Munagapati and Kim, 2016; Schmidt et al., 2019). Azo dyes have a dark color that reduces water transparency, upsets the ecological balance of the water, and even stunts the development of aquatic life (Lau et al., 2014). The detrimental effects of azo dyes or their degradation byproducts on ecosystems and human health have also been established. In addition to increasing the risk of birth defects and cancer; they could also cause high blood pressure and other random illnesses.

Numerous studies have been conducted to combat environmental damage caused by dyes. For the treatment of azo dyes wastewater, electrochemical, chemical coagulation, and biological methods are currently employed. These removal methods failed to obtain a simple and effective treatment of dye-polluted water and they could not be reliable for industrial applications, and they were also expensive (Abdel-Gawad and Abd El-Aziz, 2019; Gholami et al., 2022; Xue et al., 2023). Adsorption has become the most promising water treatment method, with a large supply of adsorbents and a simple operation (Hossam and Abdel-Aziz, 2020; Abdel-Aziz et al., 2019). To improve adsorption performance, new eco-friendly adsorbents are being produced from nanomaterials (Abdel-Aziz et al., 2021; Khan et al., 2021).

Nanotechnology offers new opportunities for advanced wastewater treatment, which is a cost-effective, and reliable way to improve the quantity and consistency of water through nano-based materials (Abdulhameed et al., 2019; Tambe Patil, 2015). Nanomaterials have recently been recognized as a wide technique of wastewater treatment, due to their unique material goods, large surface area, high reactivity, mobility solution (Mohammad et al., 2019; Wu et al., 2019), good mechanical properties, hydrophilicity, and dispersibility (Khan et al., 2018; Yaqoob and Ibrahim, 2019).

Copper nanoparticles (CuNPs) acquired a lot of attention because of their outstanding optical, electrical, catalytic, mechanical, antifungal, and antibacterial characteristics (Abdel-Aziz et al., 2021; Haider et al., 2016; Ismail et al., 2019). CuNPs offer benefits compared to other metal nanoparticles (NPs) in that they are inexpensive, have extensive availability, and in comparison to the conventional adsorbent, provide superior yields and shorter reaction times under moderate reaction conditions (Abdel-Aziz et al., 2021; Kamal et al., 2016). Consequently, this is the primary motivation to develop CuNPs.

Green synthesis is a new nanotechnology field that investigates the synthesis of nanomaterials from natural biomaterials such as plants, flowers, and microorganisms. Plant leaf extract is the most significant source of NP synthesis. In the comparison of other chemical and physical methods, green biosynthesis of NPs provides an environmentally friendly, low-cost, and safe option (Abd El-Aziz et al., 2020; Zayed et al., 2023). Recently, many techniques for the preparation of CuNPs by the leaves extract, and their applications in the removal of contaminants have been uncovered through research (Chandra and Khan, 2020). Nanoscale zero-valent copper (nZVCu) was formed from the fruit leaves extract of mature Duranta erecta to reduce azo dyes such as Congo red and methyl orange (Ismail et al., 2019). To interact with organic dyes, CuNPs were made from the extract of Nerium leaves (Sebeia et al., 2019). To cut down dyes, Ahmed et al. (2019) produced CuNPs from Camelia sinensis leaf extract. In this paper, novel zero-valent copper (Cu⁰) NPs were created utilizing an innovative green technique (Das and Debnath, 2023; Deb et al., 2023) using extracts from Ficus benjamina (FB) leaves, as shown in equation (1):

n {C u}^{+}^{2} + 2 n A r - (O H)^{n} \to n {C u}^{0} + 2 n A r = O + 2 n H^{+}

(1)

Scanning electron microscopy and Fourier transform infrared spectroscopy (FT-IR) were used to observe the Ficus nano zero valent copper (Ficus-nZVCu).

Direct Red 31 (DR31) and Direct Orange 26 (DO26) (Figure 1) were selected as the study's focus among the various anionic azo dyes; because they are widely used for dyeing polyester fabrics, and printing, making them a common industrial pollutant.

Figure 1.

(a) Molecular structure of DR31 and (b) of DO26.

However, some studies have been reported on using extracts from FB leaves to prepare nano zero valent particles as suitable adsorbents for commercial applications to remove dyes by using batch adsorption.

The main objective of this paper is to eliminate DR31 and DO26 from real samples of textile wastewater.

Materials and methods

Materials

All substances utilized were of the highest quality and analytical grade. Textile dyes DR31 (C ₃₂ H ₂₁ N ₅ Na ₂ O ₈ S ₂ ; molecular weight: 713.65), and DO26 (C ₃₃ H ₂₂ N ₆ Na ₂ O ₉ S ₂ ; molecular weight: 756.670, Anil Dyes and Chemicals Industries, India). Copper sulfate (CuSO ₄ .5H ₂ O, Sigma-Aldrich Co. Ltd); anhydrous alcohol (Fisher Scientific Co. Ltd); and Ficus leaves, were all of the highest purity and reagent grade (Ficus tree, Egypt). A digital pH meter with a microprocessor was utilized to measure the solution pH value (Jenway, UK). To balance the solution pH change, solutions of HCl and NaOH were used.

Synthesis of Ficus CuNPs (nZVCu)

Ficus leaves were washed, and dried at 105 °C; then crushed into small bits, and sieved through the sieve (2.5 mm). Plant extract of 50 g of leaves was added to 500 mL of bi-distilled water in a flask, heated (70 °C to 80 °C) for 60 min, and filtered. The solution of copper sulfate was prepared by dissolving 1.25 g of CuSO ₄ ·5H ₂ O in 50 mL of distilled water. To synthesize Ficus-nZVCu particles, 25 mL of Ficus leaf extract was added dropwise to 50 mL of Cu(II) solution, and stirred for 30–60 min to generate nanoscale particles. The color of the Ficus extract in the reaction container had changed from yellowish to brown, then to black, indicating the formation of Ficus-ZVCuNPs. The Ficus-nZVCu particles were isolated after centrifuging for 3 min. The obtained precipitate was rinsed with distilled water, and then with ethanol. Ficus-ZVCuNPs were dried at 65 °C and stored in a desiccator (Abdel-Aziz et al., 2020a). The preparation for the green Ficus-ZVCuNPs is presented in Figure 2.

Figure 2.

Preparation of Ficus nanoscale-zero valent copper (Ficus-nZVCu) particles.

Characterization of Ficus-nZVCu

A scanning electron microscope was used to observe the morphology of the Ficus-zero valent copper (Ficus-ZVCu). Before scanning, Ficus-ZVCu nanoscale particles were coated with gold, which increases electron scatter to give high contrast measuring the particle size. Energy dispersive X-ray analysis (EDAX) was used to determine the composition of the sample. FT-IR was conducted to detect the vibrational groups present. These techniques help in identifying the chemical composition of prepared Ficus-ZVCu nanoscale particles.

Point of zero charge

Approximately 18.65 g of anhydrous potassium chloride was dissolved in 2.5 L of bi-distilled H₂O. In Six 250 mL beakers, 200 mL of 0.1 M KCl solution was placed, and drop by drop the initial solution pH was adjusted with a pH meter (3510, Jenway) to solution pH 2 and 4 by 0.5 N HCl, solution pH 6 by 0.1 M HCl, solution pH 8 by 0.1 M NaOH, solution pH 10 and 12 by 0.5 N NaOH, then take 100 mL of adjusted 0.1 M KCl in a series of conical flask (solution pH_i). Approximately 50 mg of Ficus-nZVCu particles were added to each flask (solution pH 2, 4, 6, 8, 10, and 12), then the flasks were closed, and the samples were left at room temperature for 24 h. Then, the final solution pH of each suspension was measured (solution pH_f). Plotting Δsolution pH (final solution pH_f − initial solution pH_i) versus the initial solution pH value (solution pH_i) was used to find the point of zero charge (Abdel-Aziz and Fayyadh, 2020).

Batch adsorption

The stock solution of 100 mg L ⁻¹ of both DR31 and DO26 was prepared by dissolving 10 mg of dye in 100 mL water, then diluted to 1000 mL with the same solvent, and the chosen solution was prepared by a dilution of the stock solution. In each batch experiment, 50 mg L ⁻¹ of dye solution with a known concentration was mixed with 0.3 g L ⁻¹ of Ficus-ZVCuNPs. The percentages of removal were calculated using equation (2) via the spectra of dye solutions at λ_max 524 nm for DR31 and λ_max 494 nm for DO26.

Sorption [%] = [[C_{0} - C_{e}] / [C_{0}]] \times 100

(2)

where C_0, and C_e represent the initial and equilibrium concentrations of the dyes (mg L ⁻¹ ). The removal capacity at equilibrium was obtained by using equation (3):

q_{e} = [[C_{0} - C_{e}] V] / m

(3)

where V is the volume of aqueous solution (L), q _e is the equilibrium removal capacity (mg g ⁻¹ ), and m is the weight of the Ficus-nZVCuNPs (g) (Abdel-Gawad et al., 2017; Abdel-Gawad and Abdel-Aziz, 2019).

Adsorption equilibrium models

The adsorption equilibrium models are important to calculate and evaluate the various possible interactions between the DR31 and DO26 molecules with the adsorbent that occur in the adsorption process. These suggested models involved the linear Dubinin–Radushkevich, Elovich, Flory–Huggins, Fowler, Freundlich, Halsey, Harkins–Jura, Henry, Hill–Deboer, Jovanovic, Langmuir, Redlich–Peterson, Sips, Temkin, and Toth isotherm models, respectively. The equations used for different models that are used in the calculations of different adsorption parameters are depicted in Table S1 (Supplemental material).

Kinetic equilibrium models

The kinetic equilibrium isotherm models are important to investigate the mechanism of adsorption and the reaction processes that occur in the DR31 and DO26 removal processes using different possible orders of that reaction, such as pseudo-first-order, pseudo-second-order, Elovich, Fowler, intra-particle-diffusion, Avrami, Bangham, Boyd, and liquid film diffusion linear isotherm models. The equations used in different kinetic equilibrium models for studying adsorption processes in this text are shown in Table S2 (Supplemental material).

Thermodynamic study

To explore how temperature affects the removal of DR31 and DO26 dyes by Ficus-nZVCu materials, thermodynamic calculations were done to quantify the difficulty level and spontaneity of reaction processes. To examine the thermodynamics of the adsorption reaction, the experiment was done at different temperatures. The values of Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS) were calculated (Atiya et al., 2022; Abdel-Aziz et al., 2020b):

Δ G = - R T \ln K_{d}

(4)

K_{d} = q_{e} / C_{e}

(5)

Δ G = Δ H - T Δ S

(6)

Ln K_{d} = (Δ S / R) - (Δ H / R T)

(7)

where R is the universal gas constant, and K_d represents the adsorption equilibrium constant. The changes in standard enthalpy (ΔH) and standard entropy (ΔS) were calculated using the slope and intercept of 1/T versus ln K_d, respectively.

Data analysis

The experimental settings changed throughout the present study, and all experiments were run separately in triplicate with appropriate controls running simultaneously. The collected data is given a mean (average) value, and Excel 2016 software is used to make a graph out of it. Also, equations are handled with the equation editor, and references are made with the Endnote program X7.2.1, numbered style.

Linear modeling algorithms for statistical data analysis

Linear regression analysis program (SPSS24) was used to examine the effects of parameters, such as various solution pH values (4, 6, 8, and 10), different contact times (15, 30, 45, 60, 90, and 120 min), different weights of Ficus-nZVCu particles (0.2, 0.3, 0.4, and 0.5 g L ⁻¹ ), stirring rate (100, 150, 200, and 250 rev/min), temperature (298, 303, 308, and 313 K), and different concerns of dyes (25, 50, 75, and 100 mg L ⁻¹ ). Total squares and the impact of the entire model were displayed by the analysis of variance (ANOVA) program (Das et al., 2023; Saha et al., 2023).

Response surface methodologies (RSMs)

The different operational factors’ impact on the batch was studied. If the P-value is <0.05, the effect is significant for the removal procedure. If the P-value is >0.05, the effect is not significant. Deduction of dyes in the adsorption process can be assumed to follow equation (8):

R (%) = B_{o} + B_{1} \cdot X_{1} + B_{2} \cdot X_{2} + B_{3} \cdot X_{3} + B_{4} \cdot X_{4} + B_{5} \cdot X_{5} + B_{6} \cdot X_{6}

(8)

where R is the removal percent, B is constant, X ₁ is the effect of dose, X ₂ is the effect of solution pH, X ₃ is the effect of time, X ₄ is the effect of stirring rate, X ₅ is the effect of concentration, and X ₆ is the effect of temperature.

Artificial neural network (ANN)

An ANN with input, hidden, and output layers was developed using multilayer perceptron and “Multilayer Perceptron Backpropagation” to predict the efficiencies of wastewater contaminants removal. The input layer received the data from five experimental factors (dose, solution pH, time, stirring rate, initial concentration, and temperature), and the hidden layer contained different neurons. ANN models using backpropagation give the relationship between trained and tested values; they also provide the importance; and normalized importance of each effect (Abd El-Aziz et al., 2020).

Results and discussion

Characterization of Ficus-nZVCu

Different analytical and morphological tools were used to characterize and analyze the Ficus-nZVCu nano adsorbent.

Scanning electron microscopy (SEM)

SEM results of the morphological test of the prepared Ficus-nZVCu are presented in Figure 3.

Figure 3.

SEM of a prepared Ficus-nZVCu sample.

The SEM image refers to the semi-spherical form of Ficus-nZVCu with nanoscale particles. In some cases, nano agglomerations are detected and linked to the sample centrifugation procedure. Several pores are also involved in the spread and transportation of organic dye to the inner Ficus-nZVCu.

EDAX results are depicted in Figure 4, which confirms the successful synthesizing of Ficus-nZVCu particles. Cu peak point to the occurrence of nano zero-valent particles. Other peaks include Ficus extract C, O, Si, and S (Abdel-Aziz et al., 2019).

Figure 4.

EDAX of the prepared Ficus-nZVCu sample.

FT-IR measurements

The FT-IR measurements at 400 to 4000 cm ⁻¹ were used to detect polar groups in the Ficus-nZVCu adsorbent that were sharing and affecting the removal. The obtained results are presented in Figure 5.

Figure 5.

FT-IR spectrum of Ficus-nZVCu.

Polyphenol's broadband peak, which appeared at 3400 to 3100 cm ⁻¹ , is related to OH stretching vibration (Demirezen et al., 2019; Nazar et al., 2018); whereas the C=O band appeared at 1615 cm ⁻¹ (Yousef et al., 2018). The Ficus amide peak is detected at 1520 cm ⁻¹ (Zhu et al., 2018), and the polyphenolic aromatic ring C=C stretching vibration is found at 1380 cm ⁻¹ (Ramesh Kumar et al., 2014). Figure 5 refers to the presence, and the strength of phenolic compound peaks capable of reducing Cu, and serving as a reliable indicator of synthesized Ficus-nZVCu (Abdel-Aziz et al., 2019).

Point of zero charge (PZC)

The measurement of the PZC was done, and the results are shown in Figure 6.

Figure 6.

Point of zero charge of Ficus nano zero valent copper (Ficus-ZVCu) nanoparticles.

The results in Figure 6 of Ficus-nZVCu PZC values are found to be ∼6.9 for FB.

Spectrum of dyes

The absorption spectra of both DR31 and DO26 dyes (50 mg L ⁻¹ ) were measured in the wavelength range of 190–800 nm. Before the reaction, the spectrum of the dye's solution shows the major peaks of DR31 dye at 524 nm, and DO26 dye at 494 nm, respectively.

Effect of operating parameters

Effect of adsorbent dose

The adsorbent dose effect of both DR31 and DO26 dye on the removal process was studied, and the obtained results, are presented in Figure 7.

Figure 7.

The optimum effective dose and uptake on the removal.

The efficiency of DR31 and DO26 dyes removal was investigated when the doses of these dyes were altered from 0.2 to 0.5 g L ⁻¹ while keeping the rest of the operating conditions constant (solution pH initial for DR31 = 5.89 and DO26 = 5.78), time 60 min, stirring rate 100 rev/min, concentration 50 mg L ⁻¹ , and room temperature. As a result, the DR31 and DO26 dye removal percentages at altered doses are found to be (47%, 68%, 85%, and 98%) and (42%, 60%, 78%, and 94%), respectively. The optimal dosage for the elimination of DR31 and DO26 dyes was found to be ∼0.3 g L ⁻¹ . Therefore, the increased Ficus-nZVCu dose causes an increase in the number of unoccupied sites; which causes an increase in the removal percent of each dye.

The influence of solution pH on the dye adsorption process

The effect of solution pH on the removal of dyes by the tested adsorbent is investigated, and the results are presented in Figure 8.

Figure 8.

Effect of solution pH on the removal of the dye (solution pH 4, 6, 8, and 10, dose 0.3 mg L⁻¹, time 60 min, room temperature, stirring 100 rev/min, concentration 50 mg L⁻¹).

The solution pH of the dyes plays the most significant part in the dye removal efficiency procedure. DR31 and DO26 dyes were studied at different solution pHs from 4 to 10, under the selected proper conditions of a dose of Ficus-nZVCu is 0.3 g L ⁻¹ , the concentrations of the dye are 50 mg L ⁻¹ , and the stirring rate is 100 rev/min. The removal is found to vary with the variation of solution pH for DR31 (59%, 68%, 24%, and 12%), and for DO26 (48%, 60%, 21%, and 10%), Figure 8. The removal of dye is seen to increase with solution pH until it reaches solution pH 6 and then decreases. Ficus-ZVCu has a low zero charge point of 6.9. At solution pH > solution pH_pzc, the surface becomes negatively charged, which leads to repulsion with the dye constituents, resulting in low removal efficiency. Also, at a higher solution pH of 10, the removal will be reduced, which is due to the precipitated part of the dose in the removal process solution. Alternatively, the repulsion between the OH⁻ and negatively charged surfaces of NPs may exist. The attraction between the positively charged Ficus-nZVCu surface and negatively charged DR31 and DO26 dyes increased at solution pH 6; consequently, a high number of available vacant sites interfered between the NPs and the contaminant; which led to maximum dye removal. Therefore, solution pH 6 in this study is considered optimal for the removal of both DR31 and DO26 dyes (Chandra and Khan, 2020).

Effect of contact time

The effect of contact time between dyes and adsorbents is very important in the adsorption process, and the results obtained are presented in Figure 9.

Figure 9.

Effect of contact time on the removal of the dye (solution pH 6, dose 0.3 mg L⁻¹, time 15, 30, 45, 60, 90, and 120 min, room temperature, stirring 100 rev/min, concentration 50 mg L⁻¹).

At intervals of 15, 30, 45, 60, 90, and 120 min, the impact of time on effective removal was investigated. Using 0.3 g L ⁻¹ of Ficus-nZVCu at solution pH 6 and a stirring rate fixed at 100 rev/min, the percent of the dye removal for DR31 and DO26 dye concentrations of 50 mg L⁻¹ is found to be (58%, 63%, 66%, 68%, 69%, and 69%) and (50%, 56%, 59%, 60%, 61%, and 61%), respectively, as shown in Figure 9. The amount of electrically attracted molecules between the positively charged Ficus-nZVCu surface and the negatively charged dyes increased with the increase in contact time; which gradually increased the concentration of the contaminant in the empty sites of the NPs. Consequently, the removal reaches a maximum and nearly constant value. At a contact time of 30 min, the fit proportion of dye removal occurs.

Effect of stirring rate on the dye removal

Figure 10 demonstrates DR31 and DO26 dye removal by Ficus-nZVCu at a varied stirring rate of 100 to 300 rev/min at solution pH 6, time 30 min, and a dye concentration of 50 mg L ⁻¹ .

Figure 10.

Effect of stirring rate on the removal of the dye (solution pH 6, dose 0.3 mg L⁻¹, time 60 min, room temperature, stirring 100, 150, 200, 250 rev/min, concentration 50 mg L⁻¹).

The removal of DR31 and DO26 dye concentrations under these conditions is found to be (58%, 63%, 69%, and 69%) and (50%, 56%, 61%, and 61%), respectively. Therefore, the selected optimal stirring rate for the removal of both dyes is 100 rev/min.

Effect of the dye concentration

The removal experiments using Ficus-nZVCu particles were conducted on DR31 and DO26 dye solutions with different concentrations of 25, 50, 75, and 100 mg L ⁻¹ at solution pH 6 for 30 min, and the adsorbent dose was 0.3 g L ⁻¹ . The results obtained are depicted in Figure 11.

Figure 11.

Effect of concentration on the removal of the dye (solution pH 6, dose 0.3 mg L⁻¹, time 60 min, room temperature, stirring 100 rev/min, concentration: 25, 50, 75, and 100 mg L⁻¹).

The removal efficiencies for DR31 and DO26 dyes at concentrations of (25, 50, 75, and 100 mg L⁻¹), are found to be (94%, 63%, 41%, and 32%) and (89%, 56%, 38%, and 29%), respectively. Due to the abundance of empty adsorption sites at low concentrations at the start of the study, the removal ratio is high; however, as dye concentration increases gradually, this ratio decreases.

The temperature influence on the dye removal process

The impact of temperature on the rate of DR31 and DO26 dye removal from adsorption on Ficus-nZVCu particles was investigated at four different temperatures: 298, 303, 308, and 313 K, at solution pH 6, time of 30 min, concentration of 50 mg L ⁻¹ , and an adsorbent dose of 0.3 g L ⁻¹ . The results obtained are presented in Figure 12.

Figure 12.

Effect of temperature on the removal of the dye (solution pH 6, dose 0.3 mg L⁻¹, time 60 min, temperature (298, 303, 308, and 313), stirring 100 rev/min, concentration 50 mg L⁻¹).

The removal efficiencies are found to be for DR31 (63%, 64%, 65%, and 66%), and for DO26 (56%, 57%, 58%, and 59%), as revealed in Figure 12. As predicted, raising the temperature causes a little bit of removal efficiency; which means the adsorption of dyes on Ficus-nZVCu particles is to some extent endothermic in nature. The reason for increasing removal efficiency with increasing temperature is probably due to the strong adsorptive powers between the molecules of the adsorbed phases as well as between the DR31 and DO26 dyes molecules and the Ficus-nZVCu active sites (chemical adsorption). Additionally, in some circumstances, the temperature increase affects the solubility of the dye molecules; which consequently has a substantial impact on the removal efficiency (Albroomi et al., 2017; Zhu et al., 2017).

Adsorption isotherm investigations

Linear isotherm models

The results of the study of the adsorption isotherm models are shown in Table 1 and represented graphically in Figure S1 (Supplemental material). As shown in Figure 13, the Langmuir model for DR31 and DO26 dyes fits the isotherm well by having higher correlation coefficients (R²) than other linear adsorption isotherm models and a higher maximum adsorption capability.

Figure 13.

Langmuir isotherm studies for linear adsorption models (solution pH 6, dose 0.3 mg L⁻¹, time 60 min, room temperature, stirring 100 rev/min, concentration (25, 50, 75, and 100 mg L⁻¹)).

Table 1.

Linear adsorption isotherm models.

	Isotherm model	DR31	DO26
1.	Dubinin–Radushkevich	K = 1.82 × 10 ⁻³ q _m = 104.85 R ² = 0.987	K = 3.65 × 10 ⁻³ q _m = 104.85 R ² = 0.990
2.	Elovich	K = 5.84 q _m = 7.969 R ² = 0.883	K = 6.77 q _m = 7.723 R ² = 0.945
3.	Flory–Huggins	K = 0.987 n = 0.004 R ² = 0.989	K = 0.985 n = 0.005 R ² = 0.976
4.	Fowler	K = 7.7 × 10 ⁻⁷ W = −12.711 R ² = 0.917	K = 4.80 × 10 ⁻⁷ W = −13.168 R ² = 0.951
5.	Freundlich	K = 77.35 n = 12.35 R ² = 0.902	K = 69.09 n = 12.30 R ² = 0.949
6.	Halsey	K = 2.959 × 10 ⁻⁵ n = −0.135 R ² = 0.901	K = 6.931 × 10 ⁻⁵ n = −0.141 R ² = 0.955
7.	Harkins–Jura	A = 13.97 B = 2.01 R ² = 0.981	A = 15.87 B = 2.05 R ² = 0.982
8.	Henry	K = 0.341 R ² = 0.561	K = 0.290 R ² = 0.671
9.	Hill–Deboer	K ₁ = 2.33 × 10 ⁻⁴ K ₂ = −30.70 R ² = 0.969	K ₁ = 2.16 × 10 ⁻⁴ K ₂ = −30.85 R ² = 0.972
10.	Jovanovic	K = −0.004 q _m = 86.08 R ² = 0.556	K = −0.003 q _m = 79.11 R ² = 0.657
11.	Langmuir	K = 1.49 q _m = 106.38 R ² = 0.9993	K = 0.981 q _m = 98.04 R ² = 0.9999
12.	Redlich–Peterson	A = 77.35 B = 0.919 R ² = 0.9992	A = 69.096 B = 0.916 R ² = 0.9995
13.	Sips	K = 2.08 n = 1.284 R ² = 0.728	K = 1.195 n = 0.988 R ² = 0.9997
14.	Temkin	A = 33.788 B = 7.399 R ² = 0.901	A = 14.428 B = 7.110 R ² = 0.955
15.	Toth	K = 3.344 n = 0.779 R ² = 0.728	K = 1.189 n = 1.012 R ² = 0.9997

DR31: Direct Red 31; DO26: Direct Orange 26.

Non-linear isotherm models

The obtained results show that the Langmuir model fits the isotherm well by a low error sum compared to other non-linear adsorption isotherm models, as shown in Table 2 and graphically represented in Figure 14.

Figure 14.

Langmuir isotherm studies for non-linear adsorption models (solution pH 6, dose 0.3 mg L⁻¹, time 60 min, room temperature, stirring 100 rev/min, concentration (25, 50, 75, and 100 mg L⁻¹)).

Table 2.

Parameters for different non-linear isotherm models.

	Isotherm model	Error sum
	Isotherm model	DR31	DO26
1.	Freundlich	9.2953	9.8802
2.	Khan	9.0826	8.5282
3.	Langmuir	6.0741	4.717
4.	Sips	8.0828	5.0916
5.	Toth	7.5167	6.3478

DR31: Direct Red 31; DO26: Direct Orange 26.

Kinetics studies

Linear kinetics studies

The kinetics data obtained by applying different kinetic isotherm models are shown in Table 4 and graphically presented in Figure S2 (Supplemental material). According to Table 3, the particle swarm optimization (PSO) model for DR31 and DO26 dyes fits the data better than other kinetic isotherm models, as illustrated in Figure 15.

Figure 15.

Pseudo-second-order kinetic studies for linear models (solution pH 6, dose 0.3 mg L⁻¹, time (15, 30, 45, 60, 90, and 120 min), room temperature, stirring 100 rev/min, concentration 50 mg L⁻¹).

Table 3.

Parameters for different kinetic isotherm models.

	Isotherm model	DR31	DO26
1.	Pseudo-first-order	K ₁ = 0.052 q _e = 44.91 R ² = 0.9793	K ₁ = 0.054 q _e = 40.976 R ² = 0.9974
2.	Pseudo-second-order	K ₂ = 2.4 × 10 ⁻³ q _e = 119.05 R ² = 0.9999	K ₂ = 2.7 × 10 ⁻³ q _e = 105.26 R ² = 0.9999
3.	Elovich	α = 0.108 B = 25.566 × 10 ³ R ² = 0.9367	α = 0.112 B = 9.102 × 10 ³ R ² = 0.8974
4.	Intra-particle-diffusion	C = 90.387 K = 2.5466 R ² = 0.8420	C = 78.544 K = 2.4199 R ² = 0.7826
5.	Avrami	n = 0.5794 K = 0.176 R ² = 0.9572	n = 0.6362 K = 0.6.623 R ² = 0.9943
6.	Bangham	ΔB = 0.151 K = 1.975 × 10 ³ R ² = 0.9793	ΔB = 0.148 K = 1.602 × 10 ³ R ² = 0.9974
7.	Boyd	K = 0.0526 R ² = 0.9793	K = 0.0541 R ² = 0.9974
8.	Liquid film diffusion	K = 0.053 R ² = 0.9793	K = 0.054 R ² = 0.9974

DR31: Direct Red 31; DO26: Direct Orange 26.

Table 4.

Parameters for different kinetic isotherm models.

	Isotherm model	Error sum
	Isotherm model	DR31	DO26
1.	Pseudo-first-order	9.01	6.61
2.	Pseudo-second-order	4.99	3.91
3.	Avrami	8.64	9.19
4.	Elovich	7.82	8.59
5.	Intra-particle-diffusion	8.08	9.13

DR31: Direct Red 31; DO26: Direct Orange 26.

Linear kinetics studies

According to Table 4, the PSO model fits the data better than other kinetic isotherm models by a low error sum, as illustrated in Figure 16.

Figure 16.

Pseudo-second-order kinetic studies for non-linear models (solution pH 6, dose 0.3 mg L⁻¹, time (15, 30, 45, 60, 90, and 120 min), room temperature, stirring 100 rev/min, concentration 50 mg L⁻¹).

Thermodynamic study

Figure 17 shows the thermodynamic parameters that were calculated for the different adsorption processes and how they affect their rates

Figure 17.

Thermodynamic linear study for the removal of DR31 and DO26 (solution pH 6, dose 0.3 mg L⁻¹, time 60 min, temperature (298, 303, 308, and 313 K), stirring 100 rev/min, concentration 50 mg L⁻¹).

As seen, all of the values for the change in Gibbs free energy ΔG (−4.302, −4.483, −4.669, and −4.859) and (−3.581, −3.743, −3.909, and −4.081) were negative at various temperatures of 298, 303, 308, and 313 K, providing evidence that Ficus-nZVCu materials may spontaneously react with DR31 and DO26 dyes, respectively. The absolute value of ΔG increased as the temperature increased, indicating that the reaction was becoming more spontaneous and that high temperatures were favorable for the reaction, which points to a chemisorption mechanism. For DR31 and DO26 dyes, the enthalpy changed in a positive (6.77, 6.35), indicating an endothermic reaction. As a result, as the temperature goes up, the rate of both DR31 and DO26 dye reduction at equilibrium increases. In the system, the entropy ΔS change was also >0 (0.04, 0.03), showing that entropy increased during the adsorption process. When DR31 and DO26 dyes exchanged on the surface of Ficus-nZVCu materials, the entropy increased because solid–liquid interactions during the adsorption process made the system more random (Batool et al., 2018; Sahmoune, 2019).

Statistical analysis

Using the entered technique, the effect of variables on the removal processes of both DR31 and DO26 dyes was investigated and found that R ² = 0.950 and 0.932, the standard error of the estimate is 3.62 and 4.44, the study's error rate is low, and the examined factors account for nearly 95 and 93% of all the variables influencing the removal process, respectively. ANOVA program showed that the model is significant where the P value is <0.05.

Response surface methodologies

Using linear regression analysis (IBM-SPSS Statistics), the impact of various studied parameters on the adsorption processes of both dyes was investigated, and the results corroborated the practical findings. The information obtained demonstrated that all factors had an impact on the removal process except time, stirring rate, and temperature, which can be disregarded because their P values are >0.05, as shown in Table 5.

Table 5.

Statistical analysis.

Model	DR31		DO26
Model	B	Sig.	B	Sig.
[Constant]	8.940	0.004	−8.118	0.001
pH	3.787	0.059	5.833	0.023
Dose	168.088	0.000	174.448	0.000
Time	0.067	0.068	0.048	0.273
Stirring	0.002	0.916	−0.024	0.797
Conc.	−0.788	0.000	−0.730	0.000
Temp	0.066	0.763	0.010	0.971

DR31: Direct Red 31; DO26: Direct Orange 26.

The B values are used for DR31 and DO26 in equations (9) and (10) to figure out the removal equation, while:

R (%) = 8.940 + (3.787) X_{1} + (168.088) X_{2} + (0.067) X_{3} + (0.002) X_{4} + (- 0.788) X_{5} + (0.066) X_{6}

(9)

R (%) = - 8.115 + (5.833) X_{1} + (174.448) X_{2} + (0.048) X_{3} + (- 0.024) X_{4} + (- 0.730) X_{5} + (0.010) X_{6}

(10)

Artificial neural network

The multilayer perceptron neural network models 6-1-1 and 5-1-3 were used to train the ANN model for DR31 and DO26 dye removal using sample training and testing without excluding any results. Table 6 shows the sum of squared errors for testing and training, as well as the relative errors, for a total of 26 runs.

Table 6.

Model summary.

Model	DR31		DO26
Model	Training	Testing	Training	Testing
The sum of squares error	0.133	0.257	0.346	0.039
Relative error	0.015	0.056	0.041	0.014

DR31: Direct Red 31; DO26: Direct Orange 26.

Figures S2 and S5 (Supplemental material) demonstrate that there is a slight difference between the normalized value and the predicted value. Figures S3 and S6 (Supplemental material) demonstrate that there is a negligible difference between the residual value and the predicted value, demonstrating the accuracy of the model's predictions for the adsorption of two dyes onto the Ficus-nZVCu surface. Figures 18 and 19 illustrate the significance of each co-variable in the removal efficiency, with dose for DR31; concentration for DO26 being the most useful parameters, stirring rate for DR31; and temperature for DO26 being the least useful. The ANN results were in agreement with the response surface methodology findings and the experimental data.

Figure 18.

Direct Red 31 (DR31) importance for each co-variable.

Figure 19.

Direct Orange 26 (DO26) importance for each co-variable.

Reusability of FB-zero valent ion NPs

Figure 20 implies the removal efficiencies decreased with each reuse run of DR31 and DO26. Using 0.3 g L ⁻¹ of Ficus-nZVCu at solution pH 6 and a stirring rate fixed at 100 rev/min, the percent of the dye removal for DR31 and DO26 dye concentrations of 50 mg L ⁻¹ after being used in the fifth recycles. However, the removal efficiency of DR31, and DO26 was still high. Differ in the removal appropriateness, this might have been caused by the loss of nano-absorbent or by an irreversible filled with adsorption sites. The regenerated adsorbent still retained good adsorption capability after the fifth round of recycles. Results expose that, Ficus-nZVCu proposals have a high potential to be used repeatedly in removing these dyes without a significant decrease in removal suitability.

Figure 20.

Ficus nano zero valent copper (Ficus-nZVCu) particles reuse.

The co-presence of DR31 and DO26

Figure 21 depicts the effectiveness of nZVCu particles in removing the co-presence of DR31 and DO26. The absorption spectra of the mixed solution (50 mg L ⁻¹ ) of the two dyes (50:50) were measured in the range of 190 to 800 nm. This solution's light absorption spectrum shows that the major peaks of the mixed solution (DR31–DO26) were at λ_max 508 nm before the reaction. The effect of co-presence on the rate of removal of DR31 and DO26 dyes adsorption using Ficus-nZVCu particles was studied at three different wavelengths: DR31 at λ_max 524 nm, DO26 at λ_max 494 nm, and solution DR31–DO26 at λ_max 508 nm, at solution pH 6, time of 30 min, concentration of 50 mg L ⁻¹ , and adsorbent dose of 0.3 g L ⁻¹ . The removal efficiencies for DR31 (at λ_max 524 nm), DO26 (at λ_max 494 nm), and the mixed solution DR31–DO26 (at λ_max 517 nm) are found to be 35%, 30%, and 65%, respectively. The results obtained are in Figure 21.

Figure 21.

The effect of co-present on the rate of the removal.

Finally, from the observed results obtained and the presented fruitful discussion, we can strongly say that Ficus-nZVCu is an eco-friendly adsorbent that is successful in the removal of both azo dyes DR31 and DO26 from textile wastewater and produces high-quality treated effluent.

Conclusion

Green synthesis of eco-friendly Ficus-ZVCuNPs was successful utilizing low-cost Ficus benjamina leaves.

SEM and FT-IR were used to confirm the production of porous NPs.

Under optimum conditions, the Ficus-nZVCu particles are efficient nano-adsorptive agents for removing DR31 and DO26 from wastewater.

The optimum removal efficiency of DR31 dye is 63%, and that of DO26 is 56% with the following parameters, dose 0.3 g L ⁻¹ , time 30 min, concentration 50 g L ⁻¹ , and solution pH 8.

The Langmuir isotherm and PSO kinetics models fit the adsorption data well with a high R² value.

Langmuir and pseudo-second-order are more fitting in both linear and non-linear adsorption models.

The adsorption thermodynamic parameters showed the nature of spontaneity adsorption, chemisorption, endothermic processes, and random adsorption interference, which increased at the solid–solution interface and had a reasonable adsorption affinity of DR31 and DO26 dye molecules toward NPs.

The ANOVA software results revealed that nearly 95% and 93% of the variables influencing the DR31 and DO26 dye removal processes, respectively.

The co-presence of DR31 and DO26 had no impact on one another.

The regenerated adsorbent still retained fit adsorption capability after the fifth round of recycles.

Supplemental Material

sj-docx-1-adt-10.1177_02636174241256849 - Supplemental material for Adsorptive removal of Direct Red 31 and Direct Orange 26 azo dyes from aqueous solutions using Ficus nano zero valent copper: Linear, non-linear, response surface methodology (RSM), and artificial neural network (ANN) modeling

Supplemental material, sj-docx-1-adt-10.1177_02636174241256849 for Adsorptive removal of Direct Red 31 and Direct Orange 26 azo dyes from aqueous solutions using Ficus nano zero valent copper: Linear, non-linear, response surface methodology (RSM), and artificial neural network (ANN) modeling by Hossam Mohammed Abd El-Aziz, Mohamed A. Zayed and Soha Ali Abdel-Gawad in Adsorption Science & Technology

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Hossam Mohammed Abd El-Aziz

Supplemental material

Supplemental material for this article is available online.

References

Abdel-Aziz

Farag

Abdel-Gawad

(2019) Carbamazepine removal from aqueous solution by green synthesis zero-valent iron/cu nanoparticles with Ficus benjamina leaves’ extract. International Journal of Environmental Research 13: 843–852.

Abdel-Aziz

Farag

Abdel-Gawad

(2020a) Paracetamol removal by bimetallic zero-valent Fe/Cu with benjamina leaf extract. Journal of Environmental Engineering and Science 15: 119–129.

Abdel-Aziz

Farag

Abdel-Gawad

(2020b) Removal of caffeine from aqueous solution by green approach using Ficus benjamina zero-valent iron/copper nanoparticles. Adsorption Science & Technology 38: 325–343.

Abdel-Aziz

Farag

Abdel-Gawad

(2021) Removal of inorganic contaminant phosphate from aqueous solutions by bimetallic Ficus-ZVFe/Cu nanoparticles. International Journal of Environmental Engineering 11: 53–68.

Abdel-Aziz

Fayyadh

(2020) Removal of orange G by Fenton and Ficus benjamina nano zero valent iron. Journal of Environmental Engineering and Science: 1–10.

Abd El-Aziz

Farag

Abdel-Gawad

(2020) Removal of contaminant metformin from water by using Ficus benjamina zero-valent iron/copper nanoparticles. Nanotechnology for Environmental Engineering 5: 1–9.

Abdel-Gawad

Abdel-Aziz

(2019) Removal of ethinylestradiol by adsorption process from aqueous solutions using entrapped activated carbon in alginate biopolymer: Isotherm and statistical studies. Applied Water Science 9: 75.

Abdel-Gawad

Abd El-Aziz

(2019) Removal of pharmaceuticals from aqueous Medium using entrapped activated carbon in alginate. Air, Soil and Water Research 12: 1178622119848761.

Abdel-Gawad

Mohamed

Abdel Aziz

(2017) Adsorption study for chemical oxygen demand removal from aqueous solutions using alginate beads with entrapped activated carbon. Journal of Indian Water Resources Society 37: 8–16.

10.

Abdulhameed

Jawad

Mohammad

A-T

(2019) Synthesis of chitosan-ethylene glycol diglycidyl ether/TiO₂ nanoparticles for adsorption of reactive orange 16 dye using a response surface methodology approach. Bioresource Technology 293: 122071.

11.

Ahmed

Usman

Liu

Q-Y

, et al. (2019) Plant mediated synthesis of copper nanoparticles by using camelia sinensis leaves extract and their applications in dye degradation. Ferroelectrics 549: 61–69.

12.

Albroomi

Elsayed

Baraka

, et al. (2017) Batch and fixed-bed adsorption of tartrazine azo-dye onto activated carbon prepared from apricot stones. Applied Water Science 7: 2063–2074.

13.

Atiya

Hassan

Luaibi

(2022) Green synthesis of bimetallic iron/copper nanoparticles using ficus leaves extract for removing Orange G (OG) dye from aqueous medium. Nature Environment & Pollution Technology 21(1): p355.

14.

Batool

Akbar

Iqbal

, et al. (2018) Study of isothermal, kinetic, and thermodynamic parameters for adsorption of cadmium: an overview of linear and nonlinear approach and error analysis. Bioinorganic Chemistry and Applications 2018: Article ID 3463724.

15.

Chandra

Khan

(2020) Nano-scale zerovalent copper: Green synthesis, characterization and efficient removal of uranium. Journal of Radioanalytical and Nuclear Chemistry 324: 589–597.

16.

Das

Debnath

(2023) Fabrication of MgFe₂O₄/polyaniline nanocomposite for amputation of methyl red dye from water: Isotherm modeling, kinetic and cost analysis. Journal of Dispersion Science and Technology 44: 2587–2598.

17.

Das

Paul

Debnath

(2023) Fabrication of biochar from jarul (Lagerstroemia speciosa) seed hull for ultrasound aided sequestration of ofloxacin from water: Phytotoxic assessments and cost analysis. Journal of Molecular Liquids 387: 122610.

18.

Deb

Das

Debnath

(2023) Fabrication and characterization of organometallic nanocomposite for efficient abatement of dye laden wastewater: CCD optimization, adsorption mechanism, co-existing ions, and cost analysis. Chemical Physics Letters 830: 140820.

19.

Demirezen

Yıldız

YŞ

Yılmaz

, et al. (2019) Green synthesis and characterization of iron oxide nanoparticles using Ficus carica (common fig) dried fruit extract. Journal of Bioscience and Bioengineering 127: 241–245.

20.

Gholami

Llacuna

Vatanpour

, et al. (2022) Impact of a new functionalization of multiwalled carbon nanotubes on antifouling and permeability of PVDF nanocomposite membranes for dye wastewater treatment. Chemosphere 294: 133699.

21.

Haider

Kamal

Khan

, et al. (2016) Natural polymers supported copper nanoparticles for pollutants degradation. Applied Surface Science 387: 1154–1161.

22.

Hossam

SAA-G

Abdel-Aziz

(2020) Removal of sunset YellowAzo dye using activated carbon entrapped in alginate from aqueous solutions. Open Access Journal of Science 4: 1–6.

23.

Ismail

Gul

Khan

, et al. (2019) Green synthesis of zerovalent copper nanoparticles for efficient reduction of toxic azo dyes Congo red and methyl orange. Green Processing and Synthesis 8: 135–143.

24.

Kamal

Khan

Asiri

(2016) Synthesis of zero-valent Cu nanoparticles in the chitosan coating layer on cellulose microfibers: Evaluation of azo dyes catalytic reduction. Cellulose 23: 1911–1923.

25.

Khan

Naushad

Al-Gheethi

, et al. (2021) Engineered nanoparticles for removal of pollutants from wastewater: Current status and future prospects of nanotechnology for remediation strategies. Journal of Environmental Chemical Engineering 9: 106160.

26.

Khan

Sengul

Dan

(2018) Transport of TiO₂ nanoparticles and their effects on the mobility of Cu in soil media. Desalination and Water Treatment 131: 230–237.

27.

Kümmerer

(2010), Pharmaceuticals in the environment. Annual Review of Environment and Resources 35: 57–75.

28.

Lau

Y-Y

Wong

Y-S

Teng

T-T

, et al. (2014) Coagulation-flocculation of azo dye acid orange 7 with green refined laterite soil. Chemical Engineering Journal 246: 383–390.

29.

Mohammad

A-T

Abdulhameed

Jawad

(2019) Box-Behnken design to optimize the synthesis of new crosslinked chitosan-glyoxal/TiO₂ nanocomposite: Methyl orange adsorption and mechanism studies. International Journal of Biological Macromolecules 129: 98–109.

30.

Munagapati

Kim

D-S

(2016) Adsorption of anionic azo dye Congo red from aqueous solution by cationic modified orange peel powder. Journal of Molecular Liquids 220: 540–548.

31.

Nazar

Bibi

Kamal

, et al. (2018) Cu nanoparticles synthesis using biological molecule of P. granatum seeds extract as reducing and capping agent: Growth mechanism and photo-catalytic activity. International Journal of Biological Macromolecules 106: 1203–1210.

32.

Pereira

Alves

(2012). Dyes—environmental impact and remediation. In: Malik A and Grohmann E (eds) Environmental Protection Strategies for Sustainable Development. Strategies for Sustainability. Dordrecht: Springer.

33.

Ramesh Kumar

Nattuthurai

Mariappan

(2014) Biosynthesis of silver nanoparticles from Morinda tinctoria leaf extract and their larvicidal activity against Aedes aegypti Linnaeus 1762. Journal of Nanomedicine and Nanotechnology 5: 2.

34.

Saha

Gayen

Debnath

(2023) Sequestration of paracetamol from aqueous solution using zinc oxide/polypyrrole nanocomposite: Cost analysis, scale-up design, and optimization of process parameters. Journal of Hazardous, Toxic, and Radioactive Waste 27: 04023032.

35.

Sahmoune

(2019) Evaluation of thermodynamic parameters for adsorption of heavy metals by green adsorbents. Environmental Chemistry Letters 17: 697–704.

36.

Schmidt

Berghahn

Ilha

, et al. (2019) Biodegradation potential of Citrobacter cultures for the removal of amaranth and Congo red azo dyes. International Journal of Environmental Science and Technology 16: 6863–6872.

37.

Sebeia

Jabli

Ghith

(2019) Biological synthesis of copper nanoparticles, using Nerium oleander leaves extract: Characterization and study of their interaction with organic dyes. Inorganic Chemistry Communications 105: 36–46.

38.

Selvaraj

Karthika

Mansiya

, et al. (2021) An over review on recently developed techniques, mechanisms and intermediate involved in the advanced azo dye degradation for industrial applications. Journal of Molecular Structure 1224: 129195.

39.

Tambe Patil

(2015) Wastewater treatment using nanoparticles. Journal of Advanced Chemical Engineering 5: 10.4172.

40.

Tkaczyk

Mitrowska

Posyniak

(2020) Synthetic organic dyes as contaminants of the aquatic environment and their implications for ecosystems: A review. Science of the Total Environment 717: 137222.

41.

Wang

Tao

Wang

, et al. (2022) A novel hydroxyl-riched covalent organic framework as an advanced adsorbent for the adsorption of anionic azo dyes. Analytica Chimica Acta 1227: 340329.

42.

Liu

, et al. (2021) Study on the adsorption properties of methyl orange by natural one-dimensional nano-mineral materials with different structures. Scientific Reports 11: 1–11.

43.

Pang

Liu

, et al. (2019) Environmental remediation of heavy metal ions by novel-nanomaterials: A review. Environmental Pollution 246: 608–620.

44.

Xue

Gao

Seliem

, et al. (2023) Efficient adsorption of anionic azo dyes on porous heterostructured MXene/biomass activated carbon composites: Experiments, characterization, and theoretical analysis via advanced statistical physics models. Chemical Engineering Journal 451: 138735.

45.

Yaqoob

Ibrahim

MNM

(2019) A review article of nanoparticles; synthetic approaches and wastewater treatment methods. International Research Journal of Engineering Technology 6: 1–7.

46.

Yousef

Tatariants

Makarevičius

, et al. (2018) A strategy for synthesis of copper nanoparticles from recovered metal of waste printed circuit boards. Journal of Cleaner Production 185: 653–664.

47.

Zayed

Abdel-Gawad

Abdel-Aziz

, et al. (2023) Green synthesis of nano-zero-valent copper for the D-blue 60 Textile dye removal from aqueous medium. International Journal of Environmental Research 17: 12.

48.

Zhu

Ren

, et al. (2017) Effect of pH, temperature, humic acid and coexisting anions on reduction of Cr (VI) in the soil leachate by nZVI/Ni bimetal material. Environmental Pollution 227: 444–450.

49.

Zhu

Liu

, et al. (2018) Green synthesis of nano zero-valent iron/Cu by green tea to remove hexavalent chromium from groundwater. Journal of Cleaner Production 174: 184–190.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.66 MB

Adsorptive removal of Direct Red 31 and Direct Orange 26 azo dyes from aqueous solutions using Ficus nano zero valent copper: Linear,non-linear,response surface methodology (RSM),and artificial neural network (ANN) modeling

Abstract

Keywords

Introduction

Materials and methods

Materials

Synthesis of Ficus CuNPs (nZVCu)

Characterization of Ficus-nZVCu

Point of zero charge

Batch adsorption

Adsorption equilibrium models

Kinetic equilibrium models

Thermodynamic study

Data analysis

Linear modeling algorithms for statistical data analysis

Response surface methodologies (RSMs)

Artificial neural network (ANN)

Results and discussion

Characterization of Ficus-nZVCu

Scanning electron microscopy (SEM)

FT-IR measurements

Point of zero charge (PZC)

Spectrum of dyes

Effect of operating parameters

Effect of adsorbent dose

The influence of solution pH on the dye adsorption process

Effect of contact time

Effect of stirring rate on the dye removal

Effect of the dye concentration

The temperature influence on the dye removal process

Adsorption isotherm investigations

Linear isotherm models

Non-linear isotherm models

Kinetics studies

Linear kinetics studies

Linear kinetics studies

Thermodynamic study

Statistical analysis

Response surface methodologies

Artificial neural network

Reusability of FB-zero valent ion NPs

The co-presence of DR31 and DO26

Conclusion

Supplemental Material

sj-docx-1-adt-10.1177_02636174241256849 - Supplemental material for Adsorptive removal of Direct Red 31 and Direct Orange 26 azo dyes from aqueous solutions using Ficus nano zero valent copper: Linear, non-linear, response surface methodology (RSM), and artificial neural network (ANN) modeling

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

Supplemental material

References

Supplementary Material