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Scientific Reports volume 14, Article number: 17193 (2024 ) Cite this article superfine copper powder 1
The presence of heavy metals and pollutant dyes can have detrimental effects on aquatic ecosystems and compromise aquatic aesthetics. This study investigates the use of unprocessed waste gem meerschaum powder as a new adsorbent in the removal of both Cu(II) and methylene blue (MB) from aqueous solutions to reduce water pollution. The structure of the waste powder was characterized by FT-IR, XRD, SEM and BET methods. Optimization of Cu(II) and MB dye removal was carried out using design of experiment technique. Under optimum conditions, remarkable removal efficiencies of 95.5% (± 3.7) for Cu(II) and 97.8% (± 0.4) for MB were achieved. The removal of Cu(II) followed the Freundlich isotherm model, while the removal of MB dye adhered to the Langmuir isotherm model. Both adsorption processes obeyed the pseudo-second-order kinetic model and occurred spontaneously. This innovative approach offers a promising solution to water pollution by highlighting the importance of sustainable and cost-effective waste use.
Pollution of the water from dyes and heavy metals is a major environmental problem because of the potential harm to human well-being and aquatic environments1,2,3. Dye pollution in water can negatively impact the aesthetic value of water bodies, making them less attractive for recreational or tourism purposes. Dyes, are generally poorly biodegradable and thus treatment of dye-containing wastewater is a major challenge4,5,6,7. Cationic dyes, some of which are highly toxic, account for more than 10% of annual dye production entering wastewater8. In particular, cationic methylene blue (MB) dye is found as waste from the leather, textile, and dye industries. In humans, it can cause adverse consequences such as a high heart rate, diarrhea, and tissue necrosis9,10,11. Heavy metal pollution occurs when metals such as lead, mercury, cadmium, copper and arsenic are released into water bodies. These metals can have toxic effects on aquatic life and can also accumulate in the food chain, leading to harmful effects on human health12,13,14. The tendency to bioaccumulate, persistence, and non-degradability are key characteristics of potentially toxic metals. Among these, copper, while essential as a trace mineral, can be harmful in high concentrations, leading to health issues such as liver damage, kidney damage, and gastrointestinal distress. Excessive copper levels can also result in anemia and nervous system problems. Copper is often found in high concentrations in wastewater because it is considered the most valuable and widely used metal in many industrial applications such as metal plating, electroplating, plastics and etching, mining. The release of copper into water bodies can negatively affect aquatic organisms14,15. Once both dyes and heavy metals get into bodies of water, they can be difficult to remove. Therefore, it is important to prevent these pollutants from entering water bodies in the first place. By reducing dye and heavy metal pollution in water, we can protect both human health and the environment of aquatic ecosystems13. Various traditional water treatment techniques are available to reduce and recover heavy metals from aqueous solutions, including chemical precipitation16, biodegradation17, electrocoagulation18, flocculation19, and adsorption20. It is discovered that adsorption is a productive and economical technique. Adsorption has long been used for purification and separation in industrial processes21,22. The broad spectrum of adsorbent materials available is a significant advantage23. In recent years, there has been an increasing trend in using readily available, cost-effective, and environmentally friendly adsorbents. Consequently, numerous studies have explored the potential of waste materials as adsorbents24. The novelty of choosing this adsorbent is that, for the first time, waste meerschaum powders are used as adsorbents for both dyes and heavy metals. It also supports sustainability by using waste materials.
This study utilizes waste generated during meerschaum processing for jewelry, specifically waste jewelry meerschaum powder (WJMP). The primary meerschaum deposits are concentrated in Eskisehir, a city in central Turkey, with additional deposits found in Konya-Yunak. Without any pretreatment, these waste materials, including meerschaum jewelry items like necklaces, bracelets, and rosaries, were used as adsorbents25. Meerschaum is a clay mineral formed as a result of the hydration of magnesium and silicon-based bedrock pieces within the metamorphic layers at various depths of the earth, by hydrothermal effects. The German name for this mineral, “Meerschaum” (sea foam), was given because of the mineral’s density25,26,27. The formula for sepiolite, a term used in science, is natural clay mineral Si12Mg8O30(OH)4(H2O)4(8H2O) is made up of hydrous magnesium silicate. It is white in color and has an asymmetrical crystalline structure. It exists subterranean in a damp, wet state. In nature, it exists in two distinct forms. Each of them is called α-sepiolite, or “Lületaşı” in Turkish. It is amorphous, compact, and appears as pellets that resemble see-through foam27. Kıpçak et al.28 used nodular sepiolite (NS) (meerschaum) obtained from Eskişehir as an adsorbent to removal Ni(II) from water. They found the adsorption capacity for NS to be 12.15 mg g−1 at 25 °C when the optimum adsorbent dose was 0.6 g/50 mL and the optimum pH value was 6.0 with 500 mg L−1 Ni(II) solution. The adsorption of Cu(II) from aqueous solution by crude sepiolite (RS) was by Eren et al.29. The Langmuir monolayer adsorption capacity of RS was estimated at 14.96 mg g−1 in 0.1 M NaNO3 solution at 298 K. For the adsorption of MB, sepiolite-based alkali-activated material (Sep-AAM) offered a removal capability of 99.92 mg g−1 at 50 °C30.
Adsorption studies have traditionally employed a one-factor-at-a-time (OFAT) approach, where a single adsorption factor is examined while keeping other variables constant. OFAT is known to be time-consuming, requiring a large number of experiments and being costly. It lacks the ability to elucidate the impact of interactions among independent variables on the dependent variable, particularly when dealing with numerous variables31,32. On the other hand, the utilization of statistical design of experiments (DOE) offers significant advantages, including enhanced reliability and expedited outcomes compared to traditional methods. DOE enables a better understanding of the interactions between adsorbates and adsorbents while reducing the overall number of experiments required. Among the applications of DOE, response surface methodology (RSM) is a valuable and powerful technique for optimizing processes affected by multiple independent factors33,34.
The structure of WJMP has been characterized. The optimization values obtained using the experimental design method were compared with the experimentally obtained control experiments and it was seen that the selected DOE methods were compatible. RSM was applied using Box Behnken design (BBD) to remove Cu (II) and central composite design (CCD) to remove MB. It has been established that direct use of WJMP exhibits a notable capacity for adsorbing both MB and Cu(II), suggesting its potential utility as an adsorbent for various dyes and heavy metals.
Meerschaum consists of chemically stable and non-toxic aqueous magnesium silicate. Its inert nature ensures that no secondary contaminants are introduced into the solution for the removal of Cu(II) and MB dye from aqueous solutions. The dual adsorption capacity of WJMP for both heavy metals and organic dyes makes it a versatile adsorbent. Its efficiency in removing Cu(II) ions and methylene blue dye, as investigated in this study, reveals its potential for wider applications in wastewater treatment by targeting multiple contaminants simultaneously. The advantages of WJMP are its availability, cost effectiveness, environmental benefits and superior adsorption properties. This study aims to exploit these advantages to develop an efficient, sustainable and scalable solution for the removal of dye and heavy metal contaminants from aqueous solutions.
The employed WJMP adsorbent was procured from a store situated in Eskişehir, Turkey (Fig. 1). Residues generated during the crafting of jewellery and prayer beads from meerschaum were collected.
Raw material of meerschaum, processing procedure in jewellery stores, waste dust produced during jewellery-making, and diverse meerschaum jewellery and ornaments.
Throughout all the experiments, the waste materials (WJMP) were used as adsorbents for Cu(II) and MB removal without undergoing any chemical or physical treatment. Cu(NO3)2·5H2O (Merck), and Methylene blue dye (Sigma-Aldrich) were used to prepare 1000 mg L−1 stock solutions. The stock solution was diluted to create all necessary solutions at the appropriate concentrations. 0.1 mol L−1 HCl or 0.1 mol L−1 NaOH was used to change the pH using a pH meter (HANNA/pH 211 model). Using an AAnalyst 800 model Flame Atomic Absorption Spectrometer (FAAS), the concentration of Cu(II) in a solution was determined. A spectrophotometer (UV–Vis) (T80 + PG instrument LTD) was used to detect the concentration of MB in solution at the highest dye wavelength (λ = 664 nm). For mixed Cu(II) solutions with adsorbent, the N-Biotech Orbital Shaker/NB-101S model orbital shaker was utilized. The MB dye solution and adsorbent were combined using a multirotator (Biosan-Multi Bio RS-24). The adsorbent and solution were separated using the Electromag M815P centrifuge.
Fourier-Transform Infrared (FT-IR using a Bruker Tensor 27 spectrometer) analysis was conducted to determine the characteristic peaks of WJMP. Additionally, Cu(II) and MB loaded WJMP were characterized with FT-IR. WJMP's XRD crystallographic analysis was carried out on a Japanese Rigaku Miniflex II diffractometer. An accumulation rate of 0.02 min−1 was used for the X-ray analysis, which was carried out in the 2θ range from 10 to 80. Scanning electron microscopy (SEM utilizing a JEOL 50 A type scanning electron microscope) was employed to capture images. To calculate the surface areas, the MICROMERITICS/ASAP 2020 equipment was employed to apply the Brunauer–Emmett–Teller (BET) equation.
The WJMP adsorbent was employed in a batch adsorption approach for removing Cu(II) and MB from aqueous solutions. The conventional approach was used mostly to remove Cu(II) and MB from the model solutions. In Table 1 the parameters and ranges of the adsorption were given.
Every adsorption experiment was run using blank testing and repeated (N = 2). For Cu(II) and MB, the greatest standard errors recorded at the experimental points were roughly 3.00% and 1.72%, respectively.
To characterize the adsorption equilibrium, the removal percentage (R, %) and maximum adsorption capacity were calculated using Eqs. (1) and (2).
where V (mL) is the volume of the adsorbate solution, Co (mg L−1) is the initial adsorbate concentration, Ce (mg L−1) is the adsorbate concentration in solution at equilibrium, and w (g) is the adsorbent quantity2.
Adsorption isotherms are graphical representations of the relationship between the concentration of adsorbate molecules and the amount of adsorption onto a solid surface. They provide valuable information about the adsorption process at constant temperature, offering insights into porosity and surface area of the adsorbent35. The three commonly used isotherm models are Langmuir, Freundlich, and Temkin. The Langmuir isotherm suggests spontaneous and monolayer adsorption on a single, homogeneous surface36,37. It is widely used to determine maximum saturation point and constant adsorbate coverage. In contrast, the Freundlich isotherm is based on heterogeneous surfaces with various adsorption sites and changing adsorption coefficients38. Finally, the Temkin isotherm accounts for reduced adsorption due to active site availability on the solid surface. Table 2 shows the equations for these three isotherms.
Adsorption isotherms, in summary, provide critical insights into the adsorption process and are useful tools for studying the behavior of dyes and heavy metals on solid surfaces.
Adsorption kinetic models estimate how a material will adsorb a solute or other substance. Pseudo first order (PFO), pseudo second order (PSO), and Elovic are the three most common kinetic models. The formulas for several kinetic models are shown in Table 2. According to the PFO model, molecules are adsorbed equally over the surface and adsorption occurs solely by the diffusion of molecules from the bulk solution to the surface9. Because it can compute the rate at which the material adsorbs solutes, this model is useful for understanding the early stages of adsorption. Because it takes into account interactions between adsorbed molecules and the adsorbent, the PSO model is more sophisticated than the PFO model39 Eren et al.40 indicates that the PSO adsorption process consists of two phases. These two stages involve molecules diffusing from the bulk solution to the surface and an adsorption layer forming on the surface. This model aids in a better understanding of the later stages of adsorption, when there is a higher concentration of molecules adsorbed on the surface. In addition, it considers the mechanisms underlying these interactions as well as the surface dispersion of solutes. It also describes how the molecules that have been adsorbed group and interact. Stated differently, understanding the complete adsorption process from the initial phases to the last stages, when the concentration of adsorbed molecules on the surface is higher is beneficial15. Using the Elovich kinetic model, it suggests that the maximum capacity of the surface is limited by the number of accessible active sites, and the rate of adsorption decreases as the adsorbate concentration increases. The removal of dyes and heavy metals from aqueous solutions, as well as the chemical adsorption of gases to heterogeneous surfaces and solid systems, have been mostly studied with these kinetic models41.
Thermodynamic studies typically involve measuring the changes in enthalpy (ΔH), entropy (ΔS), and Gibbs free energy (ΔG) during the adsorption process. These parameters provide information on the driving forces and feasibility of the adsorption process. The thermodynamic equations were given in Table 1. The enthalpy change (ΔH) can indicate whether the adsorption process is exothermic or endothermic, while the entropy change (ΔS) can provide information on the degree of disorder or randomness during the process. The Gibbs free energy change (G) is a general indication of the process's spontaneity15. The Van't Hoff equation is commonly used to derive the thermodynamic characteristics of an adsorption process from experimental data. If the constant of equilibrium for a specific adsorption process at different temperatures is known, the equation can be utilized for determining ΔH and ΔS.
Because statistical design of experiments is more reliable and speedier than traditional methods, it allows us to gain insight into the interactions among adsorbates and adsorbents while reducing the total number of trials necessary. RSM is one of the applications of experimental design (DOE), a collection of mathematical and statistical methods for planning, developing, and improving processes. This method is a valuable and strong tool for assessing studies that demonstrate how one or more independent variables are affected by a wide range of variables in order to improve responses35.
A series of trials with various amounts of parameters such as pH, adsorbate concentration, adsorbent amount, and mixing time can be performed to improve the removal processes using the RSM approach. The outcomes from those experiments are subsequently evaluated using statistical models to determine the best combination of elements that results in the highest elimination %. As RSM models in this investigation, the Box–Behnken design (BBD) for Cu(II) removal and the central composite design (CCD) for MB removal were applied. For both models, the time and temperature parameters were held constant. Initial solution pH, adsorbate concentration, and adsorbent quantity were evaluated as variables. Both design methods employ a set of trials based on a second-order polynomial model to discover the ideal circumstances.
where (y); β0, βi (i = 1, 2, …, k); βii and βij (i = 1, 2, …, k; j = 1, 2, …, k) show the coefficients of cut, linear, square and interaction constants, respectively, x1, x2, …, xk, input variables that are effective on response, and ε random error42.
BBD/RSM approach was used to carry out the optimization study to improve Cu(II) removal capacity of WJMP. In Table 3, three independent factors and factor levels are given as coded and uncoded. Three independent variables were used in fifteen different trials. Karayünlü Bozbaş and Bingöl reported their findings and ANOVA results on the impact of variables on response or removal percentage (R, %), such as pH, adsorbent amount (m), and adsorbate concentration (Co)43.
The optimal process parameters for MB dye adsorption onto WJMP utilizing the CCD/RSM technique were determined by considering the effects of dye concentration, pH, and the amount of adsorbent. A series of experiments were conducted based on the design matrix. The response values obtained from the experiments were then used to fit a second-order polynomial model.
In Table 4, three independent factors, coded and uncoded for CCD, and factor levels are given in five levels. Conclusions and ANOVA outcomes pertaining to the impact of factors on response or removal percentage (R, %) in twenty separate trials, including pH, adsorbent quantity (m), and adsorbate concentration (Co)44.
The model was then used to identify the optimal conditions for maximum dye removal by solving for the values of the factors that give the maximum predicted response.
ANOVA can be used to assess the significance of the model created using the RSM technique. The model terms are shown to significantly affect the adsorption phenomenon when the P values are less than 0.0542.
When the XRD pattern was examined, it was observed that the WJMP material showed crystallinity in regions similar to the sepiolite material as stated in the literature and in Fig. 245,46. Sharp XRD peaks indicate a high degree of crystallinity of WJMP. The marked peaks in XRD and the raw sepiolite peaks in the literature are seen in similar places. The fact that other peaks appear at different angles is thought to be due to the crystal structure being processed and waste being formed. In addition, since the raw material XRD pattern was taken without any pre-treatment, it was observed that it gave a similar pattern with the raw and natural materials in the literature47,48. The obtained data revealed that the waste material we utilized was in its raw form, similar to natural sepiolite clay without any treatment (as it was collected immediately after jewellery processing). Remarkably, its adsorption capacity was found to be comparable to that of sepiolite49.
While the FT-IR spectrum of raw WJMP (waste material) was obtained, some material peaks were not clearly visible due to the presence of moisture. To address this, the material was dried in an oven at 65 °C and FT-IR spectra were obtained. Similarly, after the adsorption of Cu(II) and MB dye was carried out under optimum conditions, the adsorbent was dried with the same method and FT-IR spectra were recorded. The changes caused by MB dye and Cu(II) ions on the adsorbent were investigated.
The FTIR spectrum of the WJMP adsorbent is given in Fig. 3. The spectrum of the adsorbent has characteristic stretching vibrations of hydroxyl groups and OH-bend bands at 3551 and 1652 cm−139,42,43. Also, the characteristic Si–O combination bands at 1202 cm−1 and 1004, 969 and 466 cm−1 (Si–O–Si stretching bands) were seen39,50 and the intense band at 1004 cm−1 with a shoulder at 969 cm−1 is assigned to the Si–O stretching. The bands at 778, 667 and 646 cm−1 correspond to Mg3OH bending vibrations. In addition, a bending band occurs at 468 cm−147,51. In the spectrum of Cu(II) loaded adsorbent, it was observed that the peak at 1416 and 1357 cm−1 was appeared which was not found in raw WJMP spectrum. The increase in the intensity of this peak belonging to the H–OH band in the WJMP crystal may indicate that the adsorbent has weak interactions with the copper ion. At the same time, it can be stated that as a result of adsorption isotherm, Cu(II) ions accumulate on the surface as a result of physical adsorption. In addition, the disappearance of the Si–O peak in 1004 cm−1 supports the explanation of this situation. In case of MB-WJMP spectrum the peak at 1370 cm−1 found. MB dye molecules and adsorbent molecules are interacted each other on adsorbent surface. The existence of the MB molecule on the adsorbent surface induces changes in the bond vibrations and tensile energies of the adsorbent, resulting in a minor shift in the peaks observed in the spectra. MB dye molecules interact with the groups in the WJMP crystal structure, and it is also supported by the Langmuir isotherm52.
FT-IR spectrum of WJMP, Cu-WJMP, MB-WJMP.
N2 adsorption/desorption isotherm (BET) data revealed a BET surface area of 235.3567 m2 g−1 and a total pore volume for pores smaller than 27.226 Å. In addition, the average pore width determined from adsorption was measured to be 20.0884 Å.
SEM images at 500× magnification show WJMP, Cu-WJMP and MB-WJMP as shown in Fig. 4. The images reveal that the WJMP example exhibits a layered structure. However, this layered arrangement is disrupted after adsorption with MB and Cu(II). Upon MB adsorption, agglomeration is observed in the images, while the images after Cu(II) adsorption show an increase in crystal particles. In Fig. 4, the images of adsorbent and loaded adsorbent proved the above characterization results. The presence of MB and Cu(II) adsorbed on the WJMP adsorbent can also be seen in the picture from the coloration of the adsorbent.
SEM images of WJMP, MB-WJMP and Cu(II)-WJMP.
In Fig. 5, the images of adsorbent and loaded adsorbent proved the above characterization results. The presence of MB and Cu(II) adsorbed on the WJMP adsorbent can also be seen in the picture from the coloration of the adsorbent.
Images of of WJMP, Cu-WJMP, MB-WJMP.
Both dyes and heavy metal adsorption can be significantly impacted by a solution's pH. This is due to the fact that pH variations may have an impact on the surface charges of the adsorbent and adsorbate molecules53,54. Under constant experimental conditions, the effect of pH of the starting solution on the adsorption of Cu(II) and MB dye was examined. The parameters for removing Cu(II) and MB dye were established by preliminary experiments, which included 100 mg L−1 concentrations of Cu(II) and MB dye solution, adsorbent amounts of 12.5 g L−1 for Cu(II) and 0.5 g L−1 for MB dye, a temperature of 25 °C, and a contact duration of 60 min. Figure 6 illustrates how pH affects Cu(II) and MB dye adsorption.
Effect of parameters for Cu(II) and for MB dye on WJMP.
In the context of heavy metal adsorption, under high pH conditions (alkaline conditions), the surface of the adsorbent can become negatively charged, thereby attracting positively charged heavy metal ions, thereby increasing the adsorption capacity. Similarly, in the case of dye adsorption, the pH of the solution can affect both the surface charge of the adsorbent and the dye molecules. For example, at high pH levels, the surface of the adsorbent tends to carry a negative charge, resulting in increased attraction for positively charged dye molecules and, consequently, increased adsorption capacity54.
The amount of adsorbent used plays a very important role in determining the adsorption capacity. Typically, increasing the amount of adsorbent results in higher adsorption capacity due to the availability of a larger surface area for adsorption. To investigate the effect of WJMP amount, after optimization from the pH effect results, experiments were performed for Cu(II) at pH 6 and for MB removal at pH 8. The contact time, temperature and adsorbate concentration values used were the same as those in the pH effect optimization experiments. Figure 6 shows the effect of WJMP amount. In the case of heavy metal and dye adsorption, increasing the amount of adsorbent can lead to an increase in available binding sites for metal ions and dye molecules, thereby increasing the adsorption capacity. However, excessive use of the adsorbent may cause saturation of the solution and prevent further adsorption55.
The contact time between adsorbent and adsorbate also affects the adsorption capacity. Generally, adsorption increases with time until it reaches equilibrium, after which there is no further adsorption. While the contact time effects were examined at 0.250 g WJMP for Cu(II) removal and 0.030 g for MB removal, at pH 6 for Cu(II) and pH 8 for MB, other parameters were kept consistent with the pH effect optimization. Figure 6 shows the effect of contact time on both adsorbates. If the adsorption capacity is plateaued, it indicates that the adsorbent has reached its maximum adsorption capacity and cannot further remove dye molecules or heavy metal ions. The rate of equilibrium depends on the specific adsorbent-adsorbate system5.
Furthermore, increasing the adsorbate concentration generally leads to an increase in adsorption capacity as more adsorbate molecules are available for adsorption. However, the effect of adsorbate concentration on dye and heavy metal adsorption depends on several factors, such as the properties of the adsorbent surface, the chemistry of the adsorbate, and the concentration range considered. In this study, the concentration of Cu(II) and MB dye solution was changed by keeping other optimization parameters constant. The results of the concentration effect of the adsorbate are presented in Fig. 5.
Temperature also affects the adsorption of dyes and heavy metals. The effect of temperature on adsorption is complex and varies depending on the specific adsorbent-adsorbate system. Determining the optimum temperature is essential to maximize adsorption efficiency for a given system. In this study, the effect of temperature on dye and heavy metal adsorption was investigated at 25, 35 and 45 °C (Fig. 6) with optimum values determined from the above-mentioned experiments.
The Cu(II) and MB adsorption plots on WJMP examined by the three adsorption isotherm models are shown in Fig. 7 and the fitting results are shown in Table 5. It was observed that the most suitable adsorption isotherm model is Langmuir for MB and Freundlich for Cu(II). WJMP adsorbent appears to be favourable to both MB dye and Cu(II) according to the RL value in the table (0 < RL < 1).
Isotherm models for Cu(II) and MB.
The Langmuir model's linear correlation coefficients are clearly greater than 0.99, and the theoretical q value (200.65 mg g−1) is nearly equal to the experimental value (227.27 mg g−1). It suggests that the Langmuir isotherm model is appropriate for describing adsorption behavior, and monolayer adsorption of the MB dye on adsorbent surfaces has been presented. While the predicted maximum adsorption capacity of the adsorbent in Cu(II) removal was 8.44 mg g−1, the Langmuir isotherm calculation revealed that the maximum adsorption capacity of WJMP was 10.6 mg g−1. On the observed adsorbent heterogeneity surface, positively charged Cu(II) adsorption is indicated by the R2 value of Cu(II) metal, which guarantees the appropriateness and heterogeneity of the Freundlich isotherm model with a value of 0 < 1/n < 1. It demonstrates that the adsorption data for the Freundlich isotherm model is superior to that of the Langmuir and Temkin isotherm models. The applicability of the Freundlich isotherm indicates that Cu(II) and WJMP may interact intermolecularly in some situations, and that several sites with various adsorption energies are involved. Consequently, WJMP verifies the multilayer coverage of Cu(II) on the adsorbent surface with a heterogeneous limited saturation limit in adsorption affinity56.
The kinetic graphs of the adsorption process of Cu(II) and MB dye on WJMP were given in Fig. 8. The results obtained from the graphs drawn according to the PSO, PFO and Elovich kinetic models were given in Table 6. According to these results, the kinetics of both adsorbates on the adsorbent were evaluated according to the R2 values, which were compatible with the PSO kinetic model.
Kinetic models for Cu(II), MB.
Fitting the pseudo-second-order model yields theoretical qe values that are closer to the experimental values than the other models, and the correlation coefficient R2 is greater than 0.9900. This suggests that the pseudo-second-order kinetic model can accurately capture adsorption kinetics. Adsorbates can often be transported from bulk solution to adsorbent via a diffusion process.
According to the kinetic and isotherm data we acquired, along with the outcomes of our experimental optimization, we have compared our results for the removal of Cu(II) and MB from aqueous solutions with findings from existing literature that employed a similar adsorbent. The comparison is summarized in Table 7. It is evident from the table that comparable investigations have reported higher adsorption capacities for the dye compared to Cu(II).
The above equations can be used to compute the thermodynamic parameters, enthalpy change (ΔH), Gibbs free energy change (ΔG), and entropy change (ΔS). Table 8 displays the thermodynamic results. The values ΔH and ΔS can be estimated from the slopes and intercepts of the graph of lnkc versus 1/T, the results obtained for WJMP are − 5.832 kJ mol−1 for Cu(II) and − 10.544 kJ mol−1 for MB dye at 293 K. Therefore, the negative value of ΔH showed that the adsorption process was exothermic.
When ΔG is negative, it means that the adsorption procedure for WJMP is spontaneous, and as the temperature rises, so does the reaction's degree of spontaneity61. ΔG values are more negative for Cu(II) adsorption, suggesting that this adsorption process is more spontaneous. ΔG values more negative than 40 kJ mol−1 imply charge sharing or transfer from the adsorbent surface to the metal ion to form a coordinate bond, but ΔG values up to 20 kJ mol−1 are compatible with the electrostatic interaction between the adsorption sites and the metal ion62,63.
The predictive quality of the models using for optimizing Cu(II) and MB dye adsorption processes was verified ANOVA, R2, R2adj, R2pred. Factor’s effects and interactions important for Cu(II) (except for Co) and MB dye adsorption on WJMP had low P values (P < 0.05). The regression coefficients of the proposed models (R2adj) were another important evaluation criterion. The results for the reduced models were shown in Table 9 for Cu(II) and in Table 10 for MB dye. For the obtained reduced models, R2adj values for R was 99.91% for MB dye and 95.15% for Cu(II). It meant that max ~ 5% of the total variation cannot be explained by the models and it can be indicated a high agreement of actual and predicted values. According to the ANOVA results, the relationship between the response (removal percentage) and the significant effects and interactions of the factors were shown with polynomial models for Cu(II) and MB dye. According to the regression equations in coded units, positive and negative signs of coefficients indicate the synergistic and antagonistic effects of factors on the response. pH was the factor with the greatest synergistic effect for Cu(II) and MB dye.
The quadratic equation obtained from the model was graphically represented by a two-dimensional (2D) contour plot and a three-dimensional (3D) response surface (Figs. 8 and 9).
Response surface and Contour plots based on R (%) for Cu (II) and MB dye removal.
From RSM, the optimum removal of Cu(II) was found to be 95.5% at 6.0 pH, 0.40 g WJMP amount and 35 mg L−1 and initial Cu(II) concentration. For MB dye adsorption onto WJMP, the optimum removal percentage was 97.8% at 8.0 pH, 0.030 g WJMP amount and 50 mg L−1 initial MB concentration. Experimentally obtained removal percentage were 97.2% ± 0.5 for MB dye 96.2% ± 1.0 for Cu(II) with standard deviation and (N = 2).
Response surface and contour plots can provide valuable insights into the behaviour of a process and help identify the optimum conditions for achieving the desired response. When the results obtained from the classical method and the surface response and contour graphs were examined, it was determined that the percent of MB dye removal and adsorbent capacity of WJMP adsorbent was more successful than those obtained for Cu(II).
Response surface and contour plots were generated to evaluate the Cu(II) and MB dye removal percentage shown in Fig. 9. These charts provide valuable information about the relationship between variables and the efficiency of the removal process.
Meerschaum waste powder formed during jewelry making was evaluated as a potential adsorbent in the removal of Cu (II) and methylene blue (MB) from aqueous solution directly without any pretreatment. Factors affecting the adsorption process were examined with both classical and Box–Behnken and central composite design (CCD) models and the results were compared. Under optimized conditions, significant removal efficiencies of 95.5% (± 3.7) for Cu (II) and 97.8% (± 0.4) for MB were achieved. The Freundlich isotherm model best represented the removal of Cu(II), while the Langmuir isotherm best described the removal of MB dye. Both adsorption processes exhibited similar kinetic and thermodynamic properties, obeyed the pseudo-second-order kinetic model, and occurred spontaneously. The structure of waste powders was characterized by FT-IR, XRD, SEM, and BET methods. And determined by its high surface area, porous structure, high crystallinity, and the color change of the adsorbent of both Cu(II) ion and MB dye on.
This research highlights the extraordinary potential of waste for the effective removal of heavy metals and dyes from aqueous solutions. This innovative approach offers a promising solution to address water pollution, underlining the importance of sustainable and cost-effective waste use.
The data of the manuscript can be requested from the author when needed.
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Department of Chemistry, Faculty of Art and Science, Kocaeli University, 41001, Izmit, Kocaeli, Turkey
Seda Karayünlü Bozbaş & Deniz Bingöl
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S.K.B.: Conceptualization, methodology, data curation, writing—review and editing. D.B.: Conceptualization, methodology, data curation, writing. S.K.B. and D.B. contributed equally at every stage of the study.
Correspondence to Seda Karayünlü Bozbaş.
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Karayünlü Bozbaş, S., Bingöl, D. Investigation of adsorption potential of waste jewelry meerschaum powder for Cu(II) and cationic dye. Sci Rep 14, 17193 (2024). https://doi.org/10.1038/s41598-024-66050-9
DOI: https://doi.org/10.1038/s41598-024-66050-9
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