Dyes and heavy metals are present in water sources as contaminants and are harmful to human and animal health.Therefore, this study aimed to evaluate the efficacy of zinc ferrite (ZnFe2O4) nanoparticles (ZF-NPs) due to their cost-effectiveness, availability, and suitability for removal of auramine O (AO), methylene blue (MB), and cadmium (II).The effects of main operating parameters such as AO concentration, MB concentration, Cd(II) concentration, adsorbent dosage, solution pH, and sonication time were optimized by response surface methodology (RSM).Optimal conditions were obtained at adsorption of 0.25 g, pH = 6, sonication time of 15 min, and concentration of 15 mg L-1, and more than 91.56% removal from all three analytes.The adsorption of AO, MB, and Cd(II) on ZF-NPs followed pseudo-second-order kinetics, and the equilibrium data were in good agreement with the Langmuir isotherm.The maximum adsorption capacities of ZF-NPs for AO, MB and Cd(II) are as high as 201.29 mg g-1, 256.76 mg g-1 and 152.48 mg g-1, respectively.In addition, the reuse of the adsorbent was studied and it was found that the adsorbent could be used for up to five cycles.Based on the results of the interference study, it was found that the removal of AO, MB and Cd(II) by different ions was not significant under the optimal conditions.The removal of AO, MB and Cd(II) from environmental water samples by ZF-NPs was successfully studied.The results of this study suggest that ZF-NPs can serve as a suitable adsorbent to remove AO, MB and Cd(II) from aqueous solutions.
Environmental pollution caused by handling dyes and heavy metals has become one of the major global concerns, threatening ecosystem organisms and human health1,2.The increasing use of dyes and heavy metals over the past few decades has led to an increase in these compounds in groundwater and surface waters, causing serious health and ecological hazards.Due to increased water demand and limited water resources, it is necessary to prevent the release of these toxic substances into the aquatic environment and to separate them from wastewater3.
Heavy metals are stable elements that tend to accumulate in biological organisms.Heavy metals such as cadmium are also toxic in trace amounts.Heavy metal poisoning can severely damage the central nervous system, lungs, kidneys and other vital organs.Furthermore, the release of these pollutants into water sources threatens the health of aquatic animals4.Dyestuffs are one of the main pollutants present in wastewater from industries such as papermaking, plastics, leather, food and textiles, causing serious pollution to the water environment5.
The world produces about 700,000 tons of more than 10,000 dyes every year, of which about 1-15% of the dyes are discharged into wastewater during the dyeing process.Auramine O (AO) and methylene blue (MB) are the most widely used dyes in industry.Respiratory irritation, teratogenicity and carcinogenicity in humans are the main disadvantages of these compounds poisoning6,7.Additionally, the presence of these dyes in water sources can result in blocking light from entering the water, disrupting photosynthesis, and disrupting aquatic ecosystems.For these reasons, appropriate methods must be used to remove contaminants such as dyes and heavy metals8.
To remove heavy metals and dyes in aqueous solutions, there are various methods such as adsorption, ion exchange, reverse osmosis, coagulation and flocculation, advanced oxidation and ultrafiltration.Considering cost-effectiveness, adsorption is a simple, safe, ideal and economical option for removing dyes and metal ions from contaminated water14.
In the past decade, iron oxide (Fe2O3) has attracted extensive attention due to its catalytic properties to solve environmental problems15.Ferrites are magnetic compounds whose main component is iron oxide.Ease of separation by external magnetic fields after reaction and recovery is one of the most important advantages of these compounds in water and wastewater treatment.Zinc ferrite (ZnFe2O4) is widely used in water treatment processes due to its nontoxicity, high phase resistance, visible light absorption, low cost, water insolubility, and light corrosion resistance.
Wang and Shih (2021) Adsorption of methylene blue (MB) dye using ZnFe2O4/rGO nanocomposites.The results showed that the efficiency of the adsorption process increased with the increase of reaction time, pH value, and adsorbent dosage; however, increasing the initial concentration of dye significantly decreased the adsorption efficiency.Under the optimal conditions of pH = 8, initial concentration = 10 mg L−1, adsorbent dose = 10 mg, reaction time = 30 min, and temperature = 298 K, the removal efficiency reached 98%.The results show that the ZnFe2O4/rGO nanocomposite can be used as an easily accessible, environmentally friendly and cost-effective adsorbent for the removal of dyes in wastewater from various industries.
In another study, Habibi et al.(2021) Application of ZnFe2O4 to remove Congo red (CR) dye from aqueous media.In this study, the effect of different parameters such as pH, initial dye concentration and contact time on removal efficiency was evaluated.The results showed that the dye removal efficiency was the highest under the optimal conditions of pH = 3.5, adsorbent dosage = 0.006 g, dye concentration = 60 mg L-1, and contact time = 90 min.
Jethave et al.(2019) Application of Pb@ZnFe2O4 nanocomposites for removal of Congo red (CR) dye.Central composite designs are used to evaluate and optimize the effects of various factors such as pH, sorbent quality, dye concentration, and contact time.The optimized parameters (dye concentration = 150 mg L−1, pH = 7.1, stirring time = 90 min, adsorbent mass = 50 mg) resulted in a removal of 96.49% of the dye from the solution.The results show that the Pb@ZnFe2O4 nanocomposite can be used as a useful adsorbent for the treatment of water samples.
Koniki et al.(2017) evaluated ZnFe2O4 nanocomposites for the removal of rhodamine B (RB) from wastewater.The effects of various parameters such as dye concentration, reaction temperature and pH were investigated.The results show that the adsorption behavior of RB on ZnFe2O4 nanocomposites conforms to the Langmuir isotherm model, and the maximum adsorption capacity is 12.1 mg g-1.The obtained results indicate that the ZnFe2O4 nanocomposites can be used as efficient adsorbents for industrial wastewater treatment.
In recent years, the use of ultrasonic waves (ultrasound-assisted) to remove pollutants in the water environment is a new technology that has received much attention22,23.Ultrasounds are sounds above the human hearing range (20 kHz–100 MHz).Cost-effectiveness, increased removal rate, reduced removal time, and improved removal efficiency are the main advantages of ultrasound24.Due to the important application of ultrasonic technology in wastewater treatment, in this study, ZF-NPS removed pollutants in the presence of ultrasonic waves.
Design of Experiments (DOE) improves system and process performance to ensure optimal results.In the traditional method of evaluating the influence of parameters on the process, the influence between factors is not considered.In conventional methods, only one factor is changed, while the other parameters remain the same.In addition, extensive experiments are time-consuming and expensive, requiring large amounts of materials.To address these problems, multivariate statistical techniques are very beneficial, the best of which is the response surface method (RSM), which is based on a set of statistical and mathematical methods25,26,27.In this method, the optimal value of each variable is achieved by selecting the valid variables in the process and conducting experiments according to the design matrix.
In this study, the efficiency of ZF-NPs to remove AO, MB and Cd(II) with the help of ultrasound was investigated.RSM is used to model and evaluate the effects of operating parameters.Valid parameters such as pH, ZF-NPs dosage, analyte concentration, and sonication time were considered as independent variables, and percent removal (% R) was considered as the response.Efficiency of this sorbent to remove AO, MB, and Cd(II) in the presence of interfering ions in environmental water samples.RSM is used to create functions between variables and responses.Furthermore, the optimal conditions for the process were determined.
All reagents in this study include methylene blue dye (≥82%), auramine O dye (≥80%), cadmium nitrate tetrahydrate (99%), zinc acetate (≥99%), iron(III) chloride Hexahydrate (≥ 98%), hydrochloric acid (37%) and sodium hydroxide (≥ 97%) were purchased from Merck Company (Darmstadt, Germany).For the experiments, stock solutions of 1000 mg L-1 were prepared with dyes and heavy metals, respectively; working solutions were then prepared by diluting the stock solutions.Adjust pH using HCl (0.1 M) and NaOH (0.1 M) solutions and measure by pH meter.To measure the concentrations of dyes and metal ions at the experimental stage, UV-Vis spectrophotometer and atomic absorption spectroscopy (AAS) were used, respectively.An ultrasonic device equipped with a digital timer was used to disperse the adsorbent in the solvent.In addition, a centrifuge is used to accelerate the settling of the adsorbent.Characterization of ZF-NPS using Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), Fourier Transform Infrared (FT-IR) Spectroscopy, Energy Dispersive X-ray Analysis (EDX) and Brunauer-Emmett – Teller (BET) technology.
Zinc ferrite nanocrystals were prepared by hydrothermal method.To do this, mix 25 mL of iron(III) chloride hexahydrate (0.4 M) with 25 mL of zinc acetate (0.2 M).Then, 25 mL of sodium hydroxide solution (3M) was added dropwise to the above mixture.A red precipitate formed and was placed in a Teflon autoclave at 300 °C for 12 hours.Finally, the final pellet was washed several times with distilled water and dried in an oven at 80 °C.The synthesized ZnFe2O4 nanoparticles were characterized by SEM, XRD, FT-IR, BET, XPS and EDX.
In this study, experiments were performed separately for each analyte.For the adsorption process, use an Erlenmeyer flask (250 mL) filled with 100 mL of dye or heavy metal solution with a given concentration based on the experimental design performed on RSM.Adjust pH from 2 to 10 using HCl (0.1 M) and NaOH (0.1 M) solutions.In each experiment, an amount of ZF-NPS was transferred into Erlenmeyer flasks and samples were taken at the indicated times.In all experiments, ultrasonic batches were used to disperse the adsorbent in the solvent.After a specified time, the ZF-NPS magnetic adsorbent was rapidly removed by an external magnetic field.In each experiment, a specific volume of sample was taken to continue the process and centrifuged at 5,000 rpm for 5 minutes.Finally, the concentrations of dye and metal ions in the remaining solution were measured by UV-Vis spectrophotometry and atomic absorption spectroscopy (AAS) techniques, respectively.Use the equation to calculate the percent removal of analyte in the sample.1.
where C0 (mg L-1) is the analyte concentration in the initial solution, Ce (mg L-1) is the equilibrium concentration of the analyte in the solution at different times, V (L) is the solution volume, and M (g) is Weights for ZF-NPS.
The relationship between a response and the values of one or more factors is called a response surface.Response surfaces are usually represented as three-dimensional functions and are used to fit experimental data.In this approach, the level or range of the variable is determined, defining the optimal range that will result in a response close to the optimal point.At this stage, after selecting levels and approximating the sweet spot, researchers need a model that provides a high-precision approximation of the actual behavior.Given that existing response surfaces have curvature around the optimal point, a simple linear model is not sufficient to study this process.In these cases, a quadratic model fitted to the data is usually appropriate to approximate the response (Equation 3).
In this equation, y is the predicted response (percent removal) for each factor, i is the linear coefficient, j is the quadratic coefficient, β0 is the regression coefficient, βi is the linear effect, βii is the quadratic effect, and βij is the linear interaction , xi xj are the encoded values of the independent variables, k is the number of factors studied and optimized in the experiment, and e is the residual.Central Composite Designs (CCDs) are often used to create second-order polynomials.In this design, each variable is tested at five levels, and there are three different types of points, including factor points, center points, and pivot points.The factorial points are located at the vertices of the coordinate system axes and are coded as -1 and +1.The center point (0) is at the center of the design space.The axis points at a distance α from the center of the design cube lie on the axes of the coordinate system, and these points are used to calculate the curvature.
Fourier transform infrared (FT-IR) spectroscopy provides useful information about chemical groups, including highly polar bands or bands, such as C=O and OH groups, whose dipole moments change during vibrational processes.Figure 1a shows the FT-IR spectra of ZF-NPS and the ZF-NPS used.As shown in the spectra, the absorption broadband at 3420 cm-1 is attributed to the stretching vibrations of H2O molecules and OH groups on the surface of ZF-NPS.The band at ~1600 cm-1 corresponds to the bending vibrations of the H2O molecule.The band at ~2360 cm-1 indicates the adsorption of CO2 from the air, while the band at 1380 cm-1 indicates the presence of nitrates.The band at about 436 cm-1 can be attributed to the stretching vibration of octahedral Fe3+ (Fe-O mode).Furthermore, the observed band at about 564 cm-1 can be attributed to the stretching vibrations of tetrahedral Zn2+ (Zn-O mode).Furthermore, there was no difference in the peaks of the FTIR curves before and after treatment.This result indicates that ZF-NPS has excellent chemical stability and can be used as a reusable adsorbent in most applications.
(a) FT-IR spectrum, (b) X-ray diffraction pattern, (c), SEM image of fresh ZF-NPS, (d) SEM image of used ZF-NPS, and e) EDX analysis.
The sample structure was examined by X-ray diffraction (XRD) analysis.XRD patterns were recorded in the range of 2θ = 20° to 70°.The X-ray diffraction pattern of ZF-NPS (Fig. 1b) shows that the diffraction peaks at 2θ values of 30.1°, 35.3°, 43.0°, 53.3°, 56.8° and 62.8° can be determined by (220), (311), ( 400), (422), (511) and (440) facets are cubic spinel ZnFe2O4, respectively.The X-ray diffraction pattern of ZF-NPS matched well with the standard card (JCPDS: 01-077-0011).According to the ZF-NPS X-ray diffraction pattern, no impurity peaks were observed, so the expected structure was successfully formed.Furthermore, according to the Debay-Scherer equation, the approximate size of ZF-NPS is 17.23 nm.
Morphological analysis of ZF-NPS was performed by scanning electron microscopy (SEM) analysis.Figure 1c,d show the SEM images of ZF-NPS and the ZF-NPS used.In these images, irregular texture, multiple pores, and crystallinity of the samples were observed.Furthermore, the comparison of SEM images of ZF-NPS and used ZF-NPS showed that there was no significant difference between fresh and used adsorbents, which indicated that ZF-NPS was very stable during the adsorption process.
Figure 1e shows the energy dispersive X-ray (EDX) spectrum of ZF-NPS.EDX spectroscopy identifies elements present in the composition.According to Figure 1e, iron, oxygen and zinc are the only elements in the product.According to the EDX results, the percentage of each element is Fe (46.37%), O (52.18%) and Zn (1.45%).As expected, the percentage of zinc is very low and the presence of Fe and O is visible.These results indicate the synthesis of Fe2O3.
To examine the chemical composition of the surface and the elemental valence of the adsorbent, X-ray photoelectron spectroscopy (XPS) analysis was performed.Therefore, the elemental composition and chemical state of ZF-NPS before and after the reaction were investigated by XPS analysis.Figure S1a The survey XPS spectrum of ZF-NPS confirms the predominant presence of Zn, Fe and O elements.In Figure S1b, the line shape of the O 1 s core surface resembles a Gaussian with a binding energy of 530.4 eV, indicating the presence of oxygen at the ZF-NPS.Figure S1c shows two distinct peaks at 1022.5 eV and 1045.4 eV, which are attributed to the Zn 2p3/2 and Zn 2p1/2 spectra, respectively.It can be seen that the binding energies of O 1 s and Zn 2p peaks did not change significantly before and after the reaction.The peaks at 725.6 eV and 719.0 eV are attributed to the wobble satellite structures of Fe 2p1/2 and ZF-NPS, respectively (Fig. S1d).For the Fe 2p3/2 of the ZF-NPS before the reaction, the spectra were fitted with four peaks at 713.6 eV, 712.3 eV, 711.3 eV and 710.3 eV, respectively.The peak at 710.3 eV is attributed to the presence of Fe(II) oxide, and the other three peaks are attributed to Fe(III) oxide.
The structural information of ZF-NPS was obtained by N2 gas adsorption at 77.3 K in the relative pressure range (P/P0) of 0.998.The test samples were degassed for 2 hours at 120 °C.Specific surface area, total pore volume and average pore size were measured by standard BET methods.Based on this analysis, the surface area, total pore volume, and average pore size of the samples were 85.46 m2 g-1, 0.1426 cm3 g-1, and 23.8 nm, respectively.
Design-Expert software (version 10.0.6, State-Ease, Inc., USA) was used to conduct an experimental design to study the efficiency of the adsorbent in removing pollutants by the CCD method.Table 1 presents the levels of the four independent variables, including sonication time, pH, amount of sorbent, analyte concentration, and results.In this study, the total number of experiments was 30 times.To improve the accuracy and validity of the test results, each experiment was repeated 3 times.The experimental run results and software prediction results for each analyte are given in Table 1S.
In this study, the data obtained from the polynomial model were analyzed using analysis of variance (ANOVA) (Tables S2 and S3).The F-value compares the model variance and the residual variance (error) and is calculated by dividing these two parameters.The closer the variance, the lower the F-value.Therefore, a higher F value for a response indicates a more significant effect of the variable and its level on the response28,29.From Tables S2 and S3, it can be seen that the F values for removing AO, MB and Cd(II) are 261.05, 246.68 and 158.18, respectively.These values indicate the importance of the model and, moreover, the variables and their levels have a significant effect on the response.Like the F value, the P value determines the effect of the process variable on the response.The lower the p-value, the greater the statistical significance of the variable.P-values for all models are less than 0.05, which means the model and the variable are statistically significant.In general, the higher the F-value, and therefore the lower the variable’s P-value, the greater the importance of the variable30,31.According to the above table, the coefficients of determination (R2) of the three models are all greater than 99.33%, which is very close to 1.The coefficient of determination measures the proportion of change described by the model relative to the mean (the overall mean response).R2 closer to 1 means better performance and greater compatibility of the model with experimental data.An R2 greater than 99.33% means that the model describes well over 99.33% of the variability in the data, so the model cannot explain less than 0.67% of the observed variability.It is important to note that a higher R2 value does not necessarily indicate a good model, as R2 increases with more variables in the model (regardless of whether the variables are important or not).In contrast to the coefficient of determination, the adjusted coefficient (R2-Adj) is a more effective test of the adequacy of the model32.The R2-Adj values of all models were greater than 98.70%.R2-Adj is a statistic that does not increase as a variable increases and decreases even when insignificant variables are added to the model.R2-Adj is an appropriate statistic to assess the effect of reducing or increasing model variables in more complex experiments.The R2-Adj value is close to R2.The difference between these two values indicates the presence of unimportant variables in the model.The predicted R2 (R2-Pred) of all models was greater than 96.43%, indicating that the model could define or predict more than 96.43% of the variation (difference) for new data within the experimental range.Statistically, the difference between R2-Pred and R2-Adj should be less than 0.2.It can be seen that their difference in this model is much less than 0.2 and thus is acceptable.According to the results, the selected model is accurate enough for AO, MB and Cd (II) removal and provides good predictions of the results.In addition, the predicted equations for AO, MB and Cd(II) removal percentages are given in Eq.4-6.
The next step in researching and evaluating the proposed hypotheses and models is residual analysis.For statistical assumptions, it is recommended to examine normal probability plots of residuals and plots of residuals versus predicted values33,34.
Residuals are defined as the difference between the observed response (actual) and the value predicted by the model.Residuals should be checked in any ANOVA.In a normal probability plot of residuals, if the model is valid and suitable, the residuals should be unstructured.That is, their distribution should be normal and their arrangement should not follow a specific pattern.In the case of a normal distribution of errors, the general shape of the plot and the arrangement of the errors are like a straight line where the main emphasis is on the central value of the plot rather than the other points (axial and factorial).The presence of outliers is an issue that should be considered in normal probability plots, which occurs when one of the residuals is much larger than the others.One or more points that are out of range may distort the ANOVA.Figures 2a-c show residual normal probability plots for percent removal of AO, MB, and Cd (II).It can be seen that the residuals are adjacent to the straight line and have no deviation from the straight line.Also, there are no outliers, and the plot shows a normal distribution of errors.
Normal plot of residuals with removal of (a) AO, (b) MB, (c) Cd (II), residuals with removal of (d) AO, (e) MB (f) Cd (II) versus number of runs picture).
In addition, plotting the residuals according to the order of the experiments was used to determine the correlation between the residuals.The assumption of error independence is ignored in cases where there is a correlation between the residuals, in which case the accumulation of points is greater on one end of the plot than on the other.Randomly running experiments is an important factor in guaranteeing error independence, preventing this destructive effect35,36.As can be seen from Fig. 2d,f, the dots do not follow a specific pattern and are arranged in a scattered manner.Figures S2a-c show actual versus predicted responses.The points must be evenly distributed along the line so that the line passes through most of the points.The graph shows how the model will predict the data.According to the graph, the selected model can predict similar results to the experimental data.
Plot 3D and contour plots to better check the experimental design (Figure 3).In these figures, the effect of two factors on the response is investigated simultaneously.Figure 3a shows the simultaneous effects of adsorbent dosage and solution pH on AO dye removal.One of the important parameters that has a significant effect on the adsorption process is the amount of adsorbent.The effect of ZF-NPS on AO dye removal The results showed that the dye removal percentage increased as the adsorbent dosage increased from 0.01 to 0.03 g.As the amount of adsorbent increases, the number of free surfaces to be adsorbed will increase until all the dye molecules are adsorbed on the active sites on the adsorbent surface.Similar results were observed by Khan et al.(2020), they evaluated the effect of adsorbent dosage on cadmium removal.In this study, molybdenum disulfide-modified magnetic biochar was used as the adsorbent.According to the results obtained, increasing the amount of adsorbent leads to an increase in the amount of cadmium adsorption on the adsorbent37.In another study, Asfaram et al.(2017) Removal of methylene blue (MB) and malachite green (MG) dyes using Mn@CuS/ZnS-NC-AC as adsorbents.The effects of different parameters such as pH value, sonication time, MG concentration, Mn@CuS/ZnS-NC-AC dosage, and MB concentration were investigated.The results show that increasing the amount of adsorbent increases the dye removal, which is consistent with the results of this study.
3D and contour plots of the interaction effects between variables and removal (removal conditions: adsorption capacity 0.25 g, pH = 6, sonication time 15 min, concentration 15 mg L-1).
The effect of pH value on the AO dye adsorption process showed that the removal rate increased with the increase of pH value.The highest percent AO dye removal was achieved at pH 6 (Fig. 3a).The pHpzc of ZF-NPS was 5.8.Therefore, at pH > 5.8, the adsorbent surface is negatively charged, and at pH < 5.8, the adsorbent surface is positively charged.Since AO and MB dyes are cationic molecules, electrostatic repulsion between sorbent and analyte is the main reason for the improved removal at pH > pHpzc.On the other hand, at low pH, the electrostatic repulsion between the positively charged metal ions and the positive charges on the adsorbent surface reduced the Cd(II) removal rate.The metal ion removal efficiency first increased and then decreased with increasing pH due to the formation of insoluble cadmium hydroxide (Cd(OH)2) above pH 6.Bagheri et al.(2020) investigated the adsorption of lead and cadmium in aqueous solution using rGO-FeO/Fe3O4-PEI nanocomposites, and the obtained results showed that the removal percentage of lead and cadmium was reduced at alkaline pH.Palma-Anaya et al.(2017), Shi et al.(2016), Fati et al.(2015) and Rahimi et al.(2015) also obtained similar results on increasing the removal percentage by lowering pH40, 41, 42, 43.
According to Figure 3b, the removal rate decreased with increasing analyte concentration.Based on these results, the adsorbent efficiency is higher at low concentrations.At lower concentrations, more sites are available on the adsorbent surface, so the percent removal increases.At higher concentrations, as the number of adsorption sites rather than existing ions decreases, and adsorption occurs in deeper pores than the adsorbent, the adsorption process and thus the percent removal decreases.Similar results were obtained by Khalifa et al.(2020) for the removal of Cd(II), Hg(II) and Cu(II) ions using mesoporous silica nanoparticles modified with dibenzoylmethane44.Arabkhani and Asfaram (2020) applied three-dimensional magnetic polymer aerogels as adsorbents to remove MG dyes.The effects of various parameters such as adsorbent dosage, initial dye concentration, temperature, contact time and pH value of dye solution on MG removal were investigated.The results showed that the removal percentage decreased with increasing initial dye concentration, which is consistent with the results of this study.Mohebali et al.(2019) Removal of Congo red (CR) from aqueous solutions using sorbents based on natural and cetyltrimethylammonium bromide (CTAB)-modified sorbents, celery (Apium Graveolens) residues.Adsorbent dosage, pH, contact time, temperature, and dye concentration were investigated by adsorption experiments in a batch system.The results obtained show that the percentage of dye removal decreases with increasing dye concentration.
Figure 3c shows the effective interaction of pH and sonication time on MB removal.The sonication time is one of the main variables in the adsorption process.The effect of sonication time on the removal of metal ions and dyes by ZF-NPS showed that the analyte removal rate increased with increasing sonication time and was constant at higher times.Therefore, it can be concluded that the adsorption process proceeds in two stages.The fast first stage is adsorption on the adsorbent surface and the slow second stage is internal mass transfer.In the first stage, most of the adsorbent sites are empty, so the adsorption process occurs quickly on the adsorbent.Over time and the gradual filling of these sites, the permeation of analytes between adsorbed analytes and vacancies slows down, which leads to a continuous adsorption process.These results are consistent with those of Asfaram et al.(2015), who removed auramine-O (AO) dye from aqueous solutions by sonication.Asfaram et al.ZnS:Cu nanoparticles (ZnS:Cu-NP-AC) supported on activated carbon were used as adsorbents.Effective variables and designed experiments were determined by response surface methodology.The amount of adsorbent (0.02 g), sonication time (3 min), dye concentration (20 mg L-1) and pH = 7 were considered as optimal conditions.The results of this study showed that AO dye removal increased with increasing sonication time.The high adsorption capacity of ZnS:Cu-NP-AC (183.15 mg g-1) makes it a highly potential adsorbent for AO dye removal from wastewater samples47.In another study, Kumar et al.(2011) Removal of methylene blue (MB) dye from aqueous solutions using activated carbon prepared from cashew nut shells as adsorbents.In this study, the dye removal increased with increasing contact time and adsorbent dosage, which is consistent with the results of this study.
Equilibrium adsorption isotherms are critical because fundamental studies explain the interaction behavior between adsorbents and adsorption.The Langmuir (7) and Freundlich (8) equations were used to analyze isotherm data from adsorption isotherm studies, respectively.
where Ce is the equilibrium concentration of metal ions in the solution (mg L-1), and Qe is the equilibrium adsorption capacity (mg g-1).b (L mg-1) and Qmax (mg g-1) are Langmuir constants representing adsorption energy and capacity.Kf is the Freundlich constant 45, 49, 50 representing the adsorption capacity.To study the parameters of the adsorption isotherm, six different concentrations close to the optimal concentration were selected for each analyte, and the adsorption of these different concentrations was studied under optimal conditions for other valid parameters.To describe the adsorption process, the desired isotherm is determined from the correlation coefficient of the linear model of the common isotherm equation.Table 2 presents the correlation coefficient (R) and fitted model parameters for the two models.The Langmuir model’s correlation coefficient (R) for all pollutants was greater than 0.98, but less than 0.9 in Freundlich.Furthermore, the highest adsorption values determined by the Langmuir model are very close to the experimentally observed capacities for all organic dyes and heavy metal ions.The adsorption data are in good agreement with the Langmuir model, indicating that the adsorption of pollutants on ZF-NPs occurs on a single monolayer.In the Langmuir isotherm, it is assumed that the uptake of metal ions occurs at defined homogeneous adsorption sites via monolayer adsorption without interactions between adsorbed molecules.Furthermore, the maximum adsorption capacities reached 201.29 mg g-1, 256.76 mg g-1 and 152.48 mg g-1 for AO, MB and Cd(II), respectively.
Adsorption kinetics is a key parameter for describing equilibria and rates in practical use of adsorbents.The adsorption kinetics were analyzed using intraparticle diffusion kinetics, pseudo-first-order and pseudo-second-order models.Furthermore, the kinetic mechanism governing the adsorption process is described.The intraparticle diffusion (9), pseudo-first-order (10) and pseudo-second-order (11) kinetic models are expressed as:
where ki, k1, and k2 are the intraparticle diffusion rate constants (mg g-1 min-1/2), pseudo-first-order rate constants (min-1), and pseudo-second-order rate constants (g mg-1 min-1) adsorption, respectively.Qt and Qe are the adsorption capacity (mg g-1) at time t (min) and equilibrium time, respectively.C (mg g-1) represents a constant for the intraparticle diffusion model51,52,53.To study the kinetic parameters, four different times close to the optimal time were selected for each analyte and the adsorption of these different times was studied under optimal conditions for other valid parameters.To describe the adsorption mechanism, the required isotherms were determined from the correlation coefficients of the linear models of the different kinetic equations.Table 3 shows the kinetic parameters of ZF-NPs obtained from these models for various pollutants.As shown, the pseudo-second-order model of the experimental kinetic data indicated a good fit according to the correlation coefficient (R) of each kinetic model.Meanwhile, the equilibrium adsorption capacity determined according to the pseudo-second-order model is consistent with the experimental data for each pollutant.Therefore, the use of organic dyes and heavy metal ions on ZF-NPs fully fits the pseudo-second-order model compared to other models.In the rate determination step, chemisorption in a pseudo-second-order model is assumed, including valence forces by exchanging or sharing adsorbate and adsorbent electrons.Furthermore, the adsorption capacity is proportional to the number of active sites on the adsorbent surface.Therefore, it is indicated that the adsorption of pollutants by ZF-NPs is mainly chemical reaction adsorption.
PZC dots are one of the important properties that determine the surface charge of adsorbents.This parameter represents the pH of the medium surrounding the sorbent where the net positive surface charge balances with the net negative surface charge, resulting in a surface charge of zero (actually, the pH when the net surface charge is equal to zero is called PZC).To determine pHpzc values, transfer 50 mL of 0.01 M NaCl to several Erlenmeyer flasks and adjust the pH of these solutions in the range of 2-10 by 0.1 M HCl and 0.1 M NaOH.Then, 0.1 g of adsorbent was added to each flask and stirred on a shaker for 2 h.After 3 hours, measure the pH of each solution with a pH meter and plot the final pH versus the initial pH (Figure 4).The intersection of these two curves is called pHpzc, which is equal to 5.8 for ZF-NPS.
Perform these experiments and design experiments to reach the sweet spot with the highest percent removal.After obtaining responses for the 30 designed experiments, Design-Expert software analyzed the responses.In addition to the calculations performed, some new experiments are proposed as points where a more acceptable response may be obtained.These suggested points for the software are shown in Table 4.Under optimal conditions, 95.81% AO, 98.62% MB and 92.20% Cd(II) removal efficiencies were achieved using ZF-NPS (average values considered as final results).
The reusability of the sorbent is an important parameter that allows the sorbent to be reused over and over again.This feature of the adsorbent saves a lot of time and money, which in turn is a good capability, so the feature is validated.To do this, run the experiments according to the procedure described in the “Batch Experiments” section.After the reaction is complete, the sorbent particles are separated from the reaction mixture by an external magnetic field.The particles were washed several times with distilled water and dried in an oven at 60 °C.After the granules are completely dry, they can be reused by repeating the adsorption and desorption steps up to 8 times.The final concentration of dyes at each stage was determined by UV-Vis spectrophotometer, and the final concentration of heavy metals was determined by atomic absorption spectrometry (AAS).The reusability results are shown in Figure 5.The adsorbent was found to be reusable up to 5 times without any significant change in its efficiency.
Desorption performance of ZF-NPS in seven regeneration cycles (removal conditions: adsorption capacity 0.25 g, pH = 6, ultrasonic time 15 min, concentration 15 mg L-1).
To study the removal effect of ZF-NPS on AO, MB and Cd(II), various samples including tap water, Tehran University wastewater and channel water (untreated water) were used.Preliminary experiments showed that no measurable amounts of heavy metals and dyes were detected in these samples.To evaluate the applicability of the proposed method, samples were filtered and contaminated with some of each analyte.Then, measure the amount of contaminants removed under the optimal conditions mentioned in the “Batch Experiment” section.The results obtained in Table 5 show that the adsorbent has high selectivity for the removal of AO, MB and Cd(II) from environmental water samples.
Due to the presence of a large number of ions in environmental wastewater samples, the presence of these ions may have negative or positive effects on the removal of dyes and metals, this section explores the effects of the presence of these ions.For this, different concentrations of ions were added to the test solution and the removal rate under optimal conditions was measured according to the “Batch Experiments” section.Species that cause a ± 5% error in the analyte adsorption signal are called interfering ions.The obtained results are shown in Table 6.The results show that the removal of AO, MB and Cd(II) will be significantly reduced if the concentration of interfering metal ions is 100 times higher than that of the analyte.
Due to the enormous importance of water resources in human life and other organisms, the prevention of pollution of the water environment is of concern.Dyestuffs and heavy metals are among the most harmful pollutants in water sources.The method chosen to remove these contaminants should be time and cost saving with minimal damage to the environment.In this study, an adsorbent for the efficient removal of AO, MB, and Cd(II) using low-cost, available, and environmentally friendly materials is reported.For time and cost savings and minimal error response, a CCD-based RSM method was chosen for optimization.Statistical comparisons of the quadratic model for removal of dyes and heavy metals with values of p < 0.0001 and R2˃ 0.99 show that the model has reasonable accuracy.The optimal conditions were sonication time of 15 min, adsorbent dosage of 0.25 g, pH = 6, and concentration of 15 mg L-1.The ZF-NPS adsorbent could remove 95.81% of AO, 98.62% of MB and 92.20% of Cd(II) from aqueous solution under optimal conditions.These values indicate the high efficiency of the adsorbent in removing these contaminants.The pseudo-second-order model describes the adsorption kinetics well.Balanced data are in good agreement with the Langmuir model.The maximum adsorption capacities for AO, MB and Cd(II) reached 201.29 mg g-1, 256.76 mg g-1 and 152.48 mg g-1, respectively.In order to save the cost of preparing this adsorbent, its reusability was investigated.The maximum percent removal of all three analytes was found to exceed 90% after five replicate runs.The use of ZF-NPS on real samples shows that ZF-NPS can remove AO, MB and Cd(II) in the range of 83.19-97.49%.Furthermore, interference studies have shown that the removal of AO, MB and Cd(II) is significantly reduced if the concentration of the interfering metal ion is 100 times the concentration of the analyte.Therefore, according to the obtained results, ZF-NPS is an efficient, low-cost, low dosage, high durability and promising adsorbent for AO, MB and Cd(II) removal.
The authors declare that [the/all other] data supporting the findings of this study are available in the paper [and its Supplementary Information file].
Post time: Apr-26-2022