Materials and methods

Chemicals and materials

The bentonite used in this study was obtained from CMB Co. (Egypt). Magnesium chloride dihydrate (MgCl2·2H2O) and hydrochloric acid were provided by Sigma-Aldrich Co. (Egypt).

Analytical measurements for nano-bentonite and MgO-impregnated clay characterization

Magnesium-impregnated clay and nano-bentonite were characterized by scanning electron microscopy (SEM) (Quanta 250 FEI Company), transmission electron microscopy (TEM) with a JEOL-JEM-2100, Fourier-transform infrared (FTIR) spectroscopy analysis performed with a Bruker-VERTEX 80 V instrument ranging from 900 to 5 cm−1 wavenumber range, and X-Ray diffractometry (XRD) with a PANalytical X’Pert Pro(United Kingdom).

Preparation of the dye solution

The cationic dye MG (Fig. 1; chemical formula: C46H50N42C2HO4C2H204, MW: 927.1 g/mol) was purchased from MERCK Pvt. Ltd(England). A 1 g sample of the appropriate MG was dissolved in 1000 mL of distilled water to produce an MG stock solution of 1000 mg/L concentration. The stock solution was then used to prepare MG solutions of concentrations ranging from 30 to 150 mg/L. The initial pH of the stock solution was adjusted by adding to it 0.1 M HCl or NaOH. A 50 ml aliquot of the MG stock solution was used for each of the experiments. All experiments were conducted in triplicate.

Preparation of nano-bentonite

An amount of 21 g of bentonite powder and 100 mL of12 M HCl solution were combined, and the resulting mixture was heated in a magnetic stirrer at around 343 K and stirred at a rate of 340 rpm for 120 min. Subsequently, the obtained suspension was filtered and the precipitate was repeatedly washed with distilled water until the pH of the water used to wash the residue reached neutrality. The thus obtained acid-activated bentonite was dried in the oven for 5 h at a temperature of 373 K. The precipitate was then ground in a mortar to produce a powder, which was calcined in a furnace at 600 °C for 2 h31.

Fabrication of MgO-impregnated clay nanocomposite

A mixture of 7 g of bentonite clay and 100 mL of 1.25 M magnesium chloride solution was stirred for 6 h. After stirring; the solution was poured into a glass petri dish and dried in an oven at 150 °C. The dried mixture was crushed to a fine powder and calcined in a muffle furnace at 450 °C for 2 h. The calcined powder was cooled, washed twice with deionized water, and dried at 70 °C for 6 h32.

Determination of the zero point charge of the adsorbent

The pH point of zero surface charge characteristics of nano-bentonite and MgO-impregnated clay was determined using the following method33: 50 mL of 0.1 M NaCl solution was transferred into 100 mL Erlenmeyer flasks, with the initial pH (pHi) values adjusted from 3.0 to 12.0 by adding 0.1 M HCl or NaOH. Next, 0.3 g nano-bentonite and MgO-impregnated clay were added to each flask, and the suspensions were stirred continuously for 24 h. The final pH values of the supernatant liquids were assessed after 24 h. The pH PZC was plotted against the difference between the initial and final pH (pHf) values. The zero point of charge (pHZPC) of the substance was considered the point where the resulting curve intersected the pHi axis at pH = 0.

Batch adsorption experiments were carried out to achieve the optimum operating conditions for removing of the MG dye. 100 mL solution of dye initial concentration was taken in 250 mL flasks and a known amount of nano-bentonite and MgO-impregnated clay, the adsorbents were added to the solutions. The mixture was shaken mechanically at a constant speed of 200 rpm using rotary shaker (Dragon LAB, skp-0330-pro, Germany).

The effects that different experimental parameters had on the efficiency of MG removal were investigated. In particular, various values were utilized for the pH (3.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, and 11.0), contact time (10–60 min), adsorbent dosage (0.05, 0.1, 0.2, 0.5, 0.7, and 1.0 g/L), initial dye concentration (50–250 mg/L), and temperature (298, 303, 323, and 343 K). The initial pH values were adjusted using 0.1 M HCl or 0.1 M NaOH solutions and a pH meter (Multi 9620 IDS-pH meter, WTW, Germany). Each experiment was performed three times and the averages values of the measurable were calculated and presented. Samples were taken out after the equilibrium time (60 min) and centrifuged at 4000 rpm for 25 min to completely separate the nanobentonite and MgO-impregnated clay from the solution and MG concentrations in the supernatants were determined measuring the supernatants’ absorption at the wavelength at which MG exhibits its maximum absorption (λmax = 620 nm) using a spectrophotometer (Thermo Fisher Scientific, Orion Aquamat 8000, USA). MG removal efficiency, R (%), was determined through Eq. (1):

$$\%R=\frac{Co-CF}{C0}\times 100,$$
(1)

where C0 and Cf represent the initial and final concentrations of the dye solution (mg/L).

The adsorption capacity (qe, mg/g) at equilibrium, was determined using Eq. (2):

$$qe=\frac{(Ci-C)}{M}V,$$
(2)

where Ci (mg/L) and Ce (mg/L) are the MG dye concentrations in the initial solution and at equilibrium, respectively; V (L) is the volume of the solution; and w is the mass of the adsorbent (mg).

Equilibrium studies

In the current investigation, the equilibrium condition for the adsorption of MG on nano-bentonite and MgO-impregnated clay was described using the Langmuir, Freundlich, and Tempkin models, as given by34.

Kinetic studies

Pseudo-first-order and pseudo-second-order kinetic models were utilized to analyze the kinetics of MG adsorption on the adsorbents. The pseudo-primary-order model, in its linear form is described by35.

Experimental design using the response surface methodology

As a design method, the response surface methodology (RSM) is a mathematical tool that uses a second-order equation to determine the best conditions between the controllable input factors and the response variable. The effects of various factors, such as pH (X1), temperature (X2), adsorbent dosage (X3), and initial concentration (X4), on the decolorization process, were studied using the Box–Behnken design. Twenty-seven experimental runs were obtained according to the three levels of each variable; low level (− 1), level; (0) (medium) and high level (1) were used to design and analyze the experiments (Table 1). The second-order quadratic equation model was assessed to predict the optimum value between the dependent and independent factors. The correlation’s general form can be stated according to Eq. (3):

$${\text{Y}} = \beta_{{\text{o}}} + \mathop \sum \limits_{{{\text{i}} = 1}}^{{\text{n}}} \beta_{{\text{i}}} {\text{Xi}} + \mathop \sum \limits_{{{\text{i}} = 1}}^{{\text{n}}} \beta_{{{\text{ii}}}} {\text{Xi}}2 + \mathop \sum \limits_{{{\text{i}} - 1}}^{{{\text{n}} - 1}} \mathop \sum \limits_{{{\text{j}} - 1}}^{{\text{n}}} {\text{Bij}} {\text{Xi}} {\text{Xj}} .$$
(3)

Here, Y is the predicted response factor (the removal of MG), and X is the input variable. β0, βj, βjj, and βij are the intercept, linear effect, square effect, and interaction effect, respectively. N is the quantity of input-controlling coded variable. The coefficient of determination (R2) and Fisher’s F-test were used to describe the quality of the quadratic model equation. Using Design-Expert 13, an analysis of variance (ANOVA) was conducted to determine the model’s statistical significance.

Microbial toxicity

The microbial toxicity of the Malachite green dye on Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginose was investigated. Furthermore, using an agar well assay, the toxicity of the dye and its breakdown products were investigated. After 24 h of incubation at 37 °C, the zone of microbial growth inhibition was recorded.

Isolation and identification of Malachite green

A pure fungal strain was isolated from wastewater, and seven fungal strains capable of decolorizing the Malachite green dye were identified. The ability of the fungal strain to decolorize the dye was carried out in Sabroud dextrose broth SDB amended with Malachite green dye (5 mg/L). The Erlenmeyer flasks contained 100 mL sterile media with dye and were inoculated with an immobilized fungal strain. The flasks were placed in an incubator shaker for 72 h at 30 ± 2 °C. The samples were withdrawn aseptically at 24, 30, 36, 48, and 72 h alternately and centrifuged at 4500 rpm for 10 min. Furthermore, the supernatant was scanned in a spectrophotometer at λmax (620 nm) of Malachite green dye. The control flasks underwent similar former conditions, but without fungal biomass. Among the isolated strains, Mucor sp. optimally decolorized Malachite green, with a removal efficiency of 92.2%. The resultant sequence was given to the National Center for Biotechnology Information (NCBI), where it was assigned an accession number (ON934589.1). Figure 2 shows that the gene sequence was examined using NCBI’s Basic Local Alignment Search Tool (BLAST) and that a phylogenetic tree was formed using Mega 7.0.

Immobilization of Mucor sp. ON934589.1 in alginate

A sodium alginate stock solution prepared using 2 g of sodium alginate (R&M Chemicals) was dissolved in 50 mL of distilled water. Separately, bentonite was made by dissolving 1 g of bentonite and 1 g of active carbon in 50 mL of distilled water and stirring the mixture to create a homogeneous suspension. Afterward, the bentonite solution and alginate were combined and autoclaved for 20 min at 121 °C. A total of 10 g pellets of fungal cells were obtained through centrifugation (46,000 rpm for 21 min) after they were cultured in Sabroud dextrose broth. They were then combined with alginate (2% by weight) and bentonite (1% by weight) and dropped separately into 100 mL of CaCl2 solution (3% by weight) with continuous stirring. The beads formed were left for 1 h at 37 °C, washed thoroughly in distilled water, and stored for 24 h at 4 °C.

Optimization of MG decolorization using Box–Behnken design

The Box–Behnken design was used to examine the effects of four significant variables on the decolorization of MG by immobilized Mucor sp. These variables included pH (5–9) (A), temperature (25–45 °C) (B), fungal concentration (1.0, 2.0, and 3.0 g), contact time (24–72 h) (C), and initial concentrations (5–200 mg/L) (D). Flasks were kept in an incubator shaker at 120 rpm, and the optical density at λmax (620 nm) was recorded to determine the concentration of MG in the supernatant.

Model verification using the experimental data

Data were analyzed using a variety of statistical techniques, including the root mean square error (RMSE), which was calculated according to Eq. (4), where n and p are the number of experimental data and parameters number of the model, respectively. Where Pdi and Obi are predicted values and experimental data, respectively. The models for describing the maximum growth rate of Mucor sp. were evaluated using both the bias factor (Bf) and the accuracy factor (Af), as calculated according to Eqs. (5) and (6). A model is considered fail-safe if its Bf value is more than 1.0 and fail-dangerous if its Bf value is less than 1.0. On the other hand, the value of Af is never larger than 1.0, with accurate models being characterized by values for this parameter that are close to 1.0. The Akaike information criterion (AIC) is a measure of the relative quality of mathematical analyses for a given set of data, and a criterion for error prediction was calculated according to Eq. (7). The R2 formula is modified for nonlinear models to incorporate the residual mean squared error and S2y, which is the total variance of the Y-variable36.

$$\mathrm{RMSE}=\sqrt{\sum_{i-1}^{n}\left(\frac{\mathrm{experimental}/\mathrm{predicted}}{n-p}\right)2},$$
(4)
$$\mathrm{Bf}= 10exp[\mathrm{ln}10 [\sum \mathrm{log}((\mathrm{experimental}/\mathrm{predicted})/n)] ],$$
(5)
$${\text{Af}} = { }10exp\left[ {{\text{ln}}10{ }} \left[\sum \left. {\left| {{\text{log}}\left( {\left( {\frac{{{\text{experimental}}/{\text{predicted}}}}{n}} \right)} \right.} \right|} \right)\right] { } \right],$$
(6)
$${\text{AICc}} = {\text{2p }} + {\text{ nLN}}\left( {{\text{RSS}}/{\text{n}}} \right) + {2}\left( {{\text{p}} + {1}} \right) + \left( {{2}\left( {{\text{p}} + {1}} \right)\left( {{\text{p}} + {2}} \right)/{\text{n}} - {\text{p}} - {2}} \right),$$
(7)
$$\mathrm{Adjusted }\left(\mathrm{R}2 \right)= 1\frac{RMS}{S2y},$$
(8)
$${\text{Adjusted }}\left( {{\text{R2}}} \right)\, = \,{1}\, - \,({1}\, - \,R{2})\left( {{\text{n}}\, - \,{1}} \right)/\left( {{\text{n}} - {\text{p}} - {1}} \right).$$
(9)

Results and discussion

Characterization of nano-bentonite and MgO-impregnated clay

XRD patterns of nano-bentonite and MgO-impregnated clay

An XRD analysis (Fig. 3a) was conducted to determine the mineralogical constitution and crystalline nature of the nano-bentonite sample. The intensities of the XRD peaks were relatively high, which is an indication of high crystallinity. Based on the XRD pattern, we can conclude that Kaolinite-1A and quartz were the major constituents of modified bentonite, a conclusion confirmed by standard data for bentonite (ref’s: 01-075-8320 and 00-058-2028). The dominant diffraction peaks for nano-bentonite were found at values for Bragg’s angle (2θ) of ~ 12.2°,20.79°, 26.60°, ~ 27.3°, 34.88°, and. 39.43°,which are due to the presence of kaolinite, and of 19.79°, 36.47°, 42.4303°, 45.7659°, and 50.107°,which are due to the presence of quartz. The decrease of the interlayer space of nano bentonite indicates that some molecules of MG were adsorbed on top of the layers, a phenomenon that may be due to an electrostatic interaction between the positively charged groups of dye surfactant molecules with the negatively charged surface sites of nano-bentonite37,38. Scherrer's Eq. (10) has been used to calculate the crystallites' size (D):

$$D=\left(\frac{\mathrm{k\lambda }}{\upbeta }\mathrm{cos\theta }\right),$$
(10)

where D is the crystallite size, β is the full width at half maximum, λ is the X-ray wavelength, and θ is Bragg’s angle. The estimated size of the average nano-bentonite crystallite was ~ 38 nm. In Fig. 3b are reported the XRD patterns of MgO-impregnated clay. According to this figure, the said clay sample exhibited various peaks of different intensities. Indeed, peaks were observed at 2θ values of 20.91°, 26.61°, 36.57°, 37.63°, 50.14°, 56.72°, 12.27°, 18.60°, 58.76°, and 42.8392°, indicating the presence in the sample of quartzite (40%), kaolinite (10%), and MgO nanoparticles (50%), respectively. The average crystallite size was estimated to be ~ 46.6 nm. The peaks in the XRD pattern of MgO-impregnated clay generally vanished and were reduced in size, and the clay's structure changed from crystalline to slightly amorphous, demonstrating the occurrence of chemisorption processes3.

FTIR spectra of nano-bentonite and MgO-impregnated clay

The broad infrared spectroscopy bond-stretching peak between 3693.93 and 1630.21 cm−1 wave numbers (Fig. 4a) is indicative of the presence of OH stretching in hydration water on the bentonite surface. Notably, in Ref.39 detected peaks on the bentonite surface at 3450 and 1650 cm−1 wave numbers, which confirmed the existence of OH groups. In the FTIR spectra recorded in the present study. The stretching vibration of the Si–O bond was detected as avery strong absorption band at 1006 cm−1, providing strong evidence of the presence of a silicate structure. Due to the electrostatic attraction between the bentonite Si–O groups and the MG's positively charged moiety, and it indicates that the Si–O groups of bentonite may be involved in the process of dye adsorption, while the shift in the wave number values of the peaks indicates that substrate adsorption did indeed occur40. The peak at 920.80 cm−1 is attributed to the bending vibration of Al–OH–Al groups41. The presence of quartz in bentonite may be inferred from the peaks at 795 and 533 cm−1. According to42, the presence of quartz is confirmed by a band appearing at 796 cm−1. Reference43 attribute the bands at 500–400 cm−1 wave numbers to the bending vibrations of the Al–O–Si (octahedral Al) and Si–O–Si (tetrahedral Si) groups. The FTIR spectrum of the species obtained after MgO-impregnated clay underwent MG adsorption is reported in Fig. 4b. The bands at 3861 and 3622 cm−1 correspond to the stretching vibrations of the O–H bond of Si–OH groups coordinated to two Al atoms, whereas the band at 3207 cm−1 is due to MG captured by MgO. The band at 1641 cm−1 is due to the bending of water molecules, and the peak at 1423 cm−1 is attributed to the Si–O bond vibration mode. The deep band at around 1040 cm−1 is due to the stretching of the Si–O bond in the Si–O–Si groups of the tetrahedral sheet. The peak at 913 cm−1 is due to the deformation of the Al–Al–OH group; indeed, this peak is very close in position to the peaks at 913 and 914 cm−1 reported by Ref.44.The FTIR peaks appearing at 800 and 620 cm−1 are associated with Al–O + Si–O bending vibrations, while the peak at 537 cm−1 is associated with the bending vibration of the Al–O–Si group, and their observation is indicative of the presence of crystalline quartz.

TEM and SEM analyses

As can be evinced from Fig. 5a,b, the TEM images of nano-bentonite and MgO-impregnated clay indicated that these samples were irregularly shaped, heterogeneous, and semi-spherical. The surface morphologies of nano-bentonite and MgO-impregnated clay were investigated by SEM (see Fig. 5c,d, respectively). Nano-bentonite was observed to have a smooth surface and an irregular shape while surface morphology reveals a spongy appearance with an uneven structure. Additionally, micrographs of the MgO-impregnated clay powder indicate the presence of huge agglomerates of extremely fine MgO particles; these data also suggest that the said powder is highly porous. The generation of pores and voids may be caused by the bentonite clay swelling upon treatment with magnesium salt, which, upon desiccation and calcination, results in the formation of MgO clusters in the interlayer spaces of bentonite. At various magnifications, secondary electron images were acquired in order to study their morphologies and elemental compositions. The SEM image of the nano-bentonite and MgO-impregnated clay after adsorption of MG dye shows that the surface of the adsorbent is rough with an increased number of voids, as shown in Fig. 5e,f, respectively. The average crystallite sizes of MgO-impregnated clay and nano-bentonite, which were estimated via the Debye–Scherrer equation, were 46.6 and 38.9 nm, respectively, and were found to be close to the average particle size computed from individual particles: 43.2 and 34 nm, for MgO-impregnated clay and nano-bentonite, respectively. Figure 5g displays the surface morphologies of the fungus hyphae and active carbon after they have absorbed the MG. The outer surface of the fungal biomass and active carbon (AC) are coated with particles with diameters ranging from 0.1 to 1 mm, suggesting that the dyes were primarily adsorbable onto the fungus hyphae and AC. The presence of polysaccharides in the fungal biomass cell wall gives the hyphae ball a great capacity for biosorption45.

Influence of the initial MG concentration on dye adsorption

The effect of the initial concentration of MG on nano-bentonite and MgO-impregnated clay was investigated by making the said concentration vary in the 50–250 mg/L, range, while the other parameters were kept constant (contact time, 60 min; pH 7; initial concentration, 50 mg/L; agitation speed, 200 rpm; temperature, 35 °C). The dye removal efficiency of the adsorbents declined as the initial concentration of MG increased. Notably, the dye adsorption activity of MgO-impregnated clay was less influenced by changes in the initial concentration of the adsorbate than nano-bentonite. The MG removal efficiency of MgO-impregnated clay declined from 96.7 to 89.7% as the initial MG concentration increased from50 to 250 mg/L (see Fig. 9). While nano-bentonite achieved a maximum MG removal efficiency of 98.6% at an initial concentration of MG of 50 mg/L, this parameter’s value was reduced to 91.5% when the initial concentration of MG increased to 250 mg/L. This trend can probably be explained considering that the lower the initial concentration of MG, the larger the proportion of initially vacant (available) active sites on the surface of the adsorbent. Fairly similar observations were reported by Ref.2,48. Our results were in agreement with the previous study by Ref.3, which found that the iron impregnated clay's ability to remove MB dye from 98.86 to 76.80% at doses of 20–80 mg/L, respectively.

Description and analysis of the quadratic model

In order to optimize the adsorption process, a Box–Behnken design with four factors (initial concentration of dye, temperature, adsorbent dose, and pH) was chosen. The higher and lower levels of the variables are listed in Table 1, while the experimental and predicted values of the percentage decolorization of MG in the presence of nano-bentonite and MgO-impregnated clay are listed in Table 2. The second-order response surface polynomial function (Eqs. 11, 12) can be used to predict the dye's optimum operating circumstances:

\begin{aligned} {\text{Y}}({\text{nano - bentonite}}) & = 97.3092 + 2.839 \times {\text{A}} + - 0.72275 \times {\text{B}} + 0.0690833 \times {\text{C}} + - 0.0521667 \\ & \quad \times {\text{D}} + 0.69575 \times {\text{AB}} + - 0.67425 \times {\text{AC}} + 0.321 \times {\text{AD}} + - 1.136 \times {\text{BC}} + 0.8515 \times {\text{BD}} + \\ & \quad - 0.4435 \times {\text{CD}} + - 2.37618 \times {\text{A}}^{2} + - 0.276058 \times {\text{B}}^{2} + - 0.215808 \times {\text{C}}^{2} + 0.0813167 \times {\text{D}}^{2} , \\ \end{aligned}
(11)
\begin{aligned} {\text{Y}}({\text{MgO - impregnated}}\,{\text{clay}}) & = 97.4599 + 1.08619 \times {\text{A}} + - 0.952475 \times {\text{B}} + - 0.0289832 \times {\text{C}} \\ & \quad + - 0.830585 \times {\text{D}} + 0.964158 \times {\text{AB}} + - 1.15204 \times {\text{AC}} + - 1.9757 \times {\text{AD}} + 1.0302 \times {\text{BC}} \\ & \quad + 0.528359 \times {\text{BD}} + - 0.482124 \times {\text{CD}} + - 3.78607 \times {\text{A}}^{2} + - 0.135132 \times {\text{B}}^{2} + - 0.163015 \\ & \quad \times {\text{C}}^{2} + 0.188028 \times {\text{D}}^{2} . \\ \end{aligned}
(12)

The assessment of variance (ANOVA) for MG elimination efficiency in the cases of nano-bentonite and MgO-impregnated clay was applied in order to validate the model, as given in Tables 3, 4. The correlation between the variables and the responses was determined using the quadratic model and second-order polynomial analysis. The Model F-values of MG removal percentage achieved by nano-bentonite and MgO-impregnated clay were recorded as 71.81and 36.85, respectively, which were favorable. The model P-values of both models for MG removal were acceptable. Model terms are considered significant when the P-value is less than 0.0500. In this case, A, B, D, AB, AC, AD, BC, and A2 are significant model terms for MgO-impregnated clay. When the value was higher than 0.1, model terms were not considered significant. On the other hand, the model F-value of nano-bentonite was 71.81, indicating that the model was favorable. In this case, A, B, AB, AC, AD, BC, BD, CD, and A2 were satisfied model terms. The lack of fit F-value of nano-bentonite and MgO-impregnated clay were 2.62 and 0.29, respectively, implies the lack of fit is not significant relative to the pure error. There was a 22.64% and 94.48% chance for nano-bentonite and MgO-impregnated clay, respectively that a lack of fit F-value this large could be due to noise. A non-significant lack of fit indicated that the quadratic model was fit for the present study. The second-order polynomial equation was developed based on these findings to indicate a relationship between MG elimination percentage and a number of different variables. Only 0.2% and 0.9% of the total variation could not be explained by the model, according to the regression equation derived after the ANOVA, which indicated that the correlation coefficient (R2) values for the MG dye removal by nano-bentonite and MgO-impregnated clay were 0.986 and 0.973, respectively. A high R2 value (close to1) indicates that the calculated and observed findings within the experimental range are in good agreement with each other, and it also demonstrates that an acceptable and reasonable agreement with adjusted R2. The predicted R2 values for nano-bentonite and MgO-impregnated clay were 0.929 and 0.91, respectively, which are reasonably consistent with the adjusted R2 values: 0.952 and 0.947, respectively. These results demonstrated the effectiveness of the established model and the accuracy and minimal inaccuracy of the independent variable values. Adequate precision is used to determine of the signal to the noise ratio. A ratio larger than 4 is desirable. The values for this ratio were 29.5 and 22.842 for nano-bentonite and MgO-impregnated clay, respectively, indicating the reliability of the experimental data. The repeatability of the model is measured using a parameter called coefficient of variation (CV%), which is the ratio of the standard error of the estimate and the mean value of the observed response (expressed as a percentage).Typically, a model is regarded as replicable if its CV% value is less than 10%54. According to the data listed in Tables 3 and 4, the CV% values of nano-bentonite and MgO-impregnated clay are relatively small, 0.4 and 0.5%, respectively, which indicated that the deviations between experimental and predicted values were low. The plots between experimental (actual) and predicted values of MG removal by RSM model are reported in Fig. 11a,b. Based on this figure, the average differences between the predicted and experimental values can be evinced to be less than 0.1, which indicates that most of the regression model provided an explanation for the data variation.

Interpretation of variable interaction on MG removal

Three-dimensional surface plots and contour plots were generated to investigate the interaction between MG removal efficiency and two parameters at a time, while the other variables were held at constant values. The data reported in Figs. 12a,b and 13a,b demonstrate unequivocally that, as the temperature increased, so did decolorization percentage along with increasing pH. The maximum removal of MG dye decolorization from 25 to 35 °C, for nano-bentonite and 25–50 °C for MgO-impregnated clay, with increasing pH 7.0 and pH 9.0, there was a rise in the percentage of decolorization, respectively.

Effect of dose and temperature on removal of MG by nano-bentonite and MgO-impregnated clay

The elliptical shape of the curve is indicative of a high degree of interaction between the three variables. When the interaction of the MG removal efficiency with the adsorbent dosage and the adsorption temperature was examined, it was discovered that this analysis's affecting factor was temperature, as can be evinced from Figs. 12c,d and 13c,d. As the adsorbents’ dosage increased, so did the rate of MG decolorization. The temperature was found to be most influential at an nano-bentonite dosage of 0.2 g/L, in which casea79% decolorization could be observed at 25 °C and 98% decolorization could be observed at 35 °C. Maximum decolorization could be observed at a temperature of 35 °C and adsorbent dosage of 1.0 g/L. On the other hand, maximum decolorization of MG afforded by MgO-impregnated clay could be observed (97%) at a temperature of 50 °C.

Effect of pH and initial concentration on removal of MG by nano-bentonite and MgO-impregnated clay

The data reported in Figs. 12e,f and 13e,f reflect the effect that the pH and the initial MG concentration had on the percentage of MG removed, in conditions where by the temperature was kept constant. Above a specific initial MG concentration (above 300 mg/L), the adsorption capacity declines as the initial MG concentration increases, but there was a net positive interaction effect, suggesting that the adsorption capacity increases as the initial MG concentration and initial pH increase. The maximum capacities for MG adsorption by nano-bentonite and MgO-impregnated clay were observed at pH values in the 7.0–9.0 range. Thus, evidence indicates that the percentage of noxious dye elimination afforded by nano-bentonite and MgO-impregnated clay was very low at acidic pH 5.0.

Effect of dosage and initial concentration on removal of MG by nano-bentonite and MgO-impregnated clay

Referring back to Figs. 12 and 13, the combined effect on MG removal efficiency of changing the adsorbent dosage and the initial MG concentration were investigated, in conditions where by the temperature and pH were fixed at zero level. As can be evinced from Fig. 12, more than 98% and 90% of the MG dye was removed in the presence of nano-bentonite and MgO-impregnated clay under the mentioned conditions, respectively. Notably, the maximum MG removal percentage was obtained at high adsorbent dosage (0.7 g/L for nano-bentonite) and (1.0 g/L for MgO-impregnated clay), and minimum dye concentration (100 mg/L). As can be evinced from Figs. 12 and 13, dye adsorption decreased as the initial MG concentration increased. This trend may be due to the fixed number of active sites on the adsorbent vis-à-vis an increasing number of dye molecules. Banerjee and Sharma55 reported that the efficiency of dye adsorption on the adsorbents dropped significantly as the initial adsorbate concentration increased.

Kinetic studies on the adsorption of MG onto nano-bentonite and MgO-impregnated clay were conducted by fitting the experimental data with pseudo-1st-order and pseudo-2nd-order reaction rate equations.

Pseudo-first-order kinetics fitting of MG adsorption data

The experimental kinetic data were fitted with the Lagergren pseudo-first-order rate equation (Eq. 13)56,57:

$$\mathit{log}\left({q}_{e}-{q}_{t}\right)=\mathit{log}{q}_{e}-\left(\frac{{k}_{1}}{2.303}\right)t,$$
(13)

where k1 is the pseudo-primary order rate constant (min−1), qe represents the amount of MG removed at time-point t (min) of adsorbent (mg/g), and qt represents the MG adsorption capacity at equilibrium (mg/g). In Fig. 14a,b is reported the plot of log (qe − qt) versus time, whereas the relevant R2 values and constant quantity for such various adsorption kinetic designs are listed in Table 5. Given the discrepancy between the calculated (qe, cal) and experimentally determined (Expqe) adsorption capacities, which can be evinced from Table 5, a pseudo-first-order kinetics model was unable to explain the adsorption of MG onto nano-bentonite and MgO-impregnated clay. Additionally, when compared to the pseudo-second-order value, the values of the coefficient of determination (R2) were relatively small at 0.975 and 0.916, for the nano-bentonite and MgO-impregnated clay cases, respectively.

Pseudo-second-order kinetics fitting of MG adsorption data

The equation for the Lagergren pseudo-second-order kinetics (Eq. 14) is stated linearly as shown below2,57:

$$\frac{t}{{q}_{t}}= \frac{1}{{k}_{2}{q}_{e}^{2}}+ \frac{1}{{q}_{e}}\mathrm{t},$$
(14)

where k2 is the pseudo-second-order rate constant of MG adsorption (g/mg/min), and t is the contact time (min). The fitness of the straight line (R2) and the consistency between the experimental and calculated values of qe serve as indicators of each model's validity.

The plot of t/qtversus the contact time is reported in Fig. 15a,b, and the values for the relevant parameters (R2, slope, intercept, pseudo-first order rate constant) "K1", and the experimental and calculated dye uptake levels) are listed in Table 5. As can be evinced from this table, the R2 values for nano-bentonite (0.996) and MgO-impregnated clay (0.999) were quite close to 1. The computed qe values for both nano materials were in excellent agreement with the actual data, when the pseudo-second-order reaction rate equation was utilized for the computation. This observation indicates that the adsorption of MGon nano-bentonite and MgO-impregnated clay proceeds through a mechanism described by a second-order kinetics equation. According to a study conducted by Taher et al.58, the adsorption of the Congored dye onto acid-activated bentonite exhibits pseudo-second-order kinetics.

Thermodynamic study

The thermodynamic adsorption qualities depend greatly on temperature. The effect of adsorption temperature on the MG adsorption of nano-bentonite and MgO-impregnated clay was investigated at various temperatures (298, 303, 308, 323, and 343 K). During the study on the thermodynamics of dye adsorption, 50 mg/L of dye and 1 g/L of two distinct adsorbents were used at temperatures of 25 °C, 30 °C, 35 °C, 50 °C, and 70 °C.The rate Eq. (15) and the van’t Hoff equation can be used to calculate the thermodynamic parameters, such as changes in the standard free energy (G), enthalpy (H), and entropy (S), related to the adsorption process (16). The rate equation is represented as follows55,59:

$${\mathrm{lnK}}_{\mathrm{L}}= \frac{{-\Delta \mathrm{H}}^{^\circ }}{\mathrm{RT}}+ \frac{{\Delta \mathrm{S}}^{^\circ }}{\mathrm{R}},$$
(15)
$$\Delta G^\circ = \Delta H^\circ -T\Delta S^\circ .$$
(16)

Studies on the adsorption isotherms, such as the Freundlich, Langmuir, and Temkin isotherms, can be used to examine the effectiveness of the adsorbent material used for adsorption. Moreover, they can be used to determine the nature of the interaction between the adsorbed matter and the adsorbent60,61.

According to Ref.15, the Langmuir isotherm model was used to compute the maximal adsorption capacity resulting from complete monolayer coverage on the adsorbent surface and is shown as follows:

$$\frac{{C}_{e}}{{q}_{e}}= \frac{{C}_{e}}{{Q}_{max}} + \frac{1}{{Q}_{max}{K}_{L}}.$$
(17)

Here, qm is the monolayer adsorption capacity (mg/g), qe is the equilibrium adsorption amount of the adsorbate, and Ce (mg/L) is the equilibrium adsorbate concentration. Regarding the adsorption rate (L/mg), KL is the Langmuir isotherm constant. By charting Ce/qe versus Ce, the values of qm and KL at various amounts of nano-bentonite and MgO-impregnated clay can be determined in the range of 0.99 and 1.2 L/mg Fig. 17a,b. A dimensionless constant called the separation factor RL may be used to express the essential properties of a Langmuir isotherm.

$$\mathrm{RL }= 1\left(1 +\mathrm{ KL Ce}\right).$$
(18)

Here, RL is the separation term, and Co is the initial concentration of the dye solution (mg/L). The effect of the isotherm shape on “favorable” or “unfavorable” absorptions was considered62. According to the RL values between (0–1), the isotherm is either unfavorable (RL > 1), linearly favorable (RL = 1), and or irreversible (RL = 0). Results from this experiment’s use of nano-bentonite and MgO-impregnated clay were observed for RL between 0.002 and 0.009, indicating that the adsorption was irreversible favorable. Table 7 shows the findings of MG removal on nano-bentonite and MgO-impregnated clay using the Langmuir model. The R2 in Table 7 showed strong positive proof of the adsorption of MG ion adsorbents following the Langmuir isotherm. The suitability of the linear form of the Langmuir model to nano-bentonite was confirmed through the high correlation coefficients R2 > 0.992.Conversely, the linear form of the Langmuir model to MgO-impregnated clay was slightly fit with the regression coefficients (R2) value (0.962%). This shows that the Langmuir isotherm can provide a decent sorption model. Moreover, the adsorption capacities of the nano-bentonite and MgO-impregnated clays were 13.8 and 17.2 mg/g, respectively. This result corresponds with6, who discovered that the adsorption capacity of CuFe2O4 for MG is 22 mg/g.

According to the Freundlich isotherm model, adsorption occurs on a heterogeneous surface with a non-uniform heat distribution over the adsorbent surface. The linearized form of the Freundlich model is given as follows63:

$$ln{q}_{e}=ln{K}_{F}+\frac{1}{n}ln{C}_{e}.$$
(19)

Tempkin isotherm

The adsorption energy changes and the surface of the adsorbent toward the adsorption of different species in diverse mixes were assessed using the Tempkin adsorption isotherm. The R2 value and decreased error analysis were effective and efficient criteria. The model has typically been used in the following format (Eq. 20):

$${q}_{e}=\frac{RT}{b}\mathit{ln}\left({K}_{T}\right)+\frac{RT}{b}ln\left({C}_{e}\right).$$
(20)

Here, β = (RT)/b,Tis the absolute temperature in Kelvin, and R is the universal gas constant(8.314 J/(mol K)); the constant β correlates with the heat of adsorption60. As shown in Table 7 and Fig. 19a,b, applying the experimental equilibrium data to Eq. (20) demonstrated excellent and reasonable applicability of the model in explaining and interpreting MG adsorption on nano-bentonite and MgO-impregnated clay. In the Temkin isotherm, positive AT values of 1.4 L/mg and 2.1 L/mg for nano-bentonite and MgO-impregnated clay, respectively, showed that the process was endothermic. The Temkin model also showed a high R2 value, indicating a chemisorption process rather than a physisorption one. The results obtained correspond with those reported by Gündüz60. Furthermore, the R2 values realized using the Tempkin model were similar to those observed using the Langmuir and Freundlich equations.

Parameters for the performance and optimization of MG adsorption

Analyzing the interaction between working factors using conventional methods is challenging. Consequently, predictions of operational factors and their synergistic impact are frequently based on assumptions. Operational factors can be simultaneously and efficiently analyzed, and the degree of interaction can be assessed using RSM. Due to the operational factor screening, the ideal conditions for MG adsorption were determined. The optimum condition of processing factors for MG adsorption was obtained at pH 9,with an initial concentration of 50 mg/L and a 4.0 g/L adsorbent dosage within 35 °C for nano-bentonite. Compared with MgO-impregnated clay, the optimum condition of processing factors was obtained at pH 9, with an initial concentration of 50 mg/L anda4.0 g/L adsorbent dosage within 40 °C. Under these circumstances, the high decolorization efficiency was 97.53% and 93.9% for nano-bentonite and MgO-impregnated clay, respectively.

Optimization of Malachite green decolorization using statistical design

Supplementary Table 1 shows that the Box–Behnken design with four variables (pH, initial concentration, contact time, and temperature) was used to improve the decolorization process. Supplementary Table 2 shows the experimental and predicted values of the percentage decolorization. The second-order response surface polynomial function allowed the prediction of ideal dye operating conditions. Figure 20a,b shows that the experimental response values for MG decolorization correspond with the predicted response values, the normal probability, and the studentized residual plot.

\begin{aligned} & {\text{Y}} = 87.98 + 2.09167 \times {\text{A}} + - 0.153333 \times {\text{B}} + - 0.0158333 \times {\text{C}} + 1.53917 \times {\text{D}} + - 2.5 \times {\text{AB}} \\ & \quad + - 7.25 \times {\text{AC}} + 1.575 \times {\text{AD}} + 4.61 \times {\text{BC}} + 7.265 \times {\text{BD}} + 0.0925 \times {\text{CD}} + - 18.4612 \\ & \quad \times {\text{A}}^{2} + - 9.34375 \times {\text{B}}^{2} + - 5.145 \times {\text{C}}^{2} + - 4.3425 \times {\text{D}}^{2} \\ \end{aligned}
(21)

According to Table 9, the ANOVA results for the quadratic regression model indicated that the model was significant. The superior F value (60.99) and reduced P values (< 0.0500) of Malachite green show that the model terms were significant. The variables A, D, AB, AC, BD, BD, A2, B2, C2, and D2 were determined to be significant model terms for decolorization based on the P values. Furthermore, according to the results of the ANOVA in Supplementary Table 2, the linear effects of the dye temperature, pH, and concentration were found to be increasingly important for MG dye decolorization. According to the “lack of fit F value of 0.715,”the lack of fit was insignificant regarding the pure error. An insignificant lack of fit was regarded as a reliable indicator that the model would be good. The fit of the model was also expressed by the coefficient of regression R2.The predicted R2 of immobilized Mucor sp.was 0.9837, which is consistent with the adjusted R2 of 0.967. These findings indicate that the developed model was satisfactory and that the values of the independent factors were accurate with minimal error. The range of the projected response regarding the associated error was measured with adequate precision. A ratio of at least 4 is acceptable; however, a ratio higher than 4 is preferable. The ratio of 24.1 for Mucor sp. was high, indicating the reliability of the experimental data. Moreover, according to Supplementary Table 2, the coefficient of variation (CV%) values for Mucor sp. obtained in the study are relatively small, with 2.6. This indicated that the deviations between the experimental and predicted values were low. Figure 20c shows a graph of the Box-Cox diagram of model changes in MG removal (%) using Mucor sp. composite determined by a quadratic polynomial. The best lambda value (λ = 1.49) is between the two red vertical lines, so no data transformation is required. The red line shows the minimum (− 0.2900) and maximum (3.32) values, as well as lambdas at 95% confidence interval value.

Interactive impact of pH on MG dye decolorization

The biosorption of Malachite green by the fungus was investigated for a pH range of 5–9.

The maximum degree of decolorization (97.8%) was reached at a pH of 7.0, while at a pH of 9, the decolorization rate decreased to 40%. Figure 21a,b shows that the efficacy of dye decolorization using immobilized Mucor sp. decreased with rising pH levels. Moreover, Fig. 21c shows that the removal efficiency was 54% at a pH of 5.0 and 30 °C, and it improved to 87.8% at a pH of 7 and 30 °C. Similar findings were made by Ref.64, who discovered that the efficiency of decolorization of Malachite green using Aspergillus niger was about 97% at a pH of 7.

Interactive impact of temperature on MG dye decolorization

Various environmental factors influenced the degradation of Malachite green using Mucor sp. This fungal strain degraded Malachite green effectively from 298 to 303 °C (Fig. 21c,e,f). Figure 21c,e,f shows that the rate of Malachite green decolorization increases as the temperature rises from 25 to 30 °C. Furthermore, Malachite green decolorization by Mucor sp. ON934589.1 reached a maximum of 91.54% at 303 °C. Conversely, as the temperature rose to 313 °C, the decolorization activity decreased (53%) due to the loss of cell viability or inactivation of the decolorizing enzymes65. Arunprasath et al.4 observed that the optimum temperature (30 °C) was the optimum temperature for the decolorization (92%) of Malachite green dye by Lasiodiplodia strains.

Interactive impact of concentration on MG dye decolorization

The adsorption behavior of MG was studied in concentrations of 5–200 mg/L at a pH of 7.0. In addition, 87.7–97.4% of Malachite green was eliminated by the immobilized fungus at 5–100 mg/L. The decolorization efficiency of Mucor sp. (ON934589.1) fell below 64% when the starting concentration of Malachite green approached 150 mg/L. These findings suggest that high concentrations of Malachite green impede the development of Mucor sp. (ON934589.1). Figure 21b shows the impact of the initial concentration and contact time on the removal of Malachite green dye using immobilized fungus. The immobilized fungus was suppressed at 150 mg/L of Malachite green due to the existence of sulfonic acid on the aromatic ring formed in the medium by the increased concentration of Malachite green, which inhibited the nucleic acid synthesis and microbial cell proliferation66. The findings of this investigation corresponded with those of Ref.67, who found that Aspergillus fumigates immobilized in polyurethane foam had an optimum Malachite green decolorization percentage of about 97.52% (40 mg/L),which reduced to 23% at 70 mg/L.

Impact of contact time on MG dye decolorization

At the optimum dye concentration (50 mg/L) and biosorbent dosage (6 g/L), the impact of contact time on adsorption was examined from 24 to 72 h. Figure 21a,d shows the influence of contact time on the elimination of the MG dye. The range of the MG’s adsorption efficiency was 18 to 72 h, corresponding to 72% and 97%, respectively. Based on the data, 40 h was determined to be the equilibrium time in the sorption process because no further improvement was observed after reaching maximum adsorption. The high removal efficiency at the beginning of the contact time of 40 h was due to the large surface area available for dye adsorption during the initial stage, and the adsorbent’s capacity gradually depleted over time, as the few remaining vacant surface sites became tough to occupy because of repulsive forces between the solute molecules on the solid and bulk phases68,69. Our results correspond with those of Ref.70, who observed that Lasiodiplodia sp. could decolorize 81% of Malachite green within 36 h.

The Monod model is used to represent the relationship between the limiting substrate concentration and the specific growth rate using Eqs. (19) and (20).

$$\mu =\frac{\mu maxS}{S+KS},$$
(22)
$$\mu =\frac{1}{x}\frac{dx}{dt}.$$
(23)

Here, µ, µmax, and Ks were determined as the biodegradation experiment data. The magnitude of Yx/s was estimated from the slope of the graph of dX dt versus S. The original Monod model becomes inadequate when a substrate prevents biodegradation. Monod derivatives with substrate inhibition adjustment Eqs. (24 and 32), suggested by the Haldane, Aiba–Edward, Luong, Han, and Levenspiel models, have been used to assess the impacts of inhibition at a high substrate concentration and stimulation at a low concentration of substrate71,73,74,74. Here, S and μ are the substrate concentration and the specific growth rate, respectively; μmax is the maximum specific growth rate; n and m are experimental constants; Ks is the half substrate saturation coefficient, and Sm is the critical inhibitor concentration (mg/L) above which growth ceases.

$$q=\frac{1}{X}\frac{ds}{dt},$$
(24)
$$q=\frac{QmaxS}{K2+S},$$
(25)
$$\frac{dy}{dx}=-\frac{\mathrm{qmaxSX}}{\mathrm{Ks}+\mathrm{S}},$$
(26)
$$\frac{dy}{dx}=-Yx/s\frac{ds}{dt},$$
(27)
$$\upmu =Yx/sQ,$$
(28)
$$\mu = \frac{umaxS}{{S + Ks + [1 - \left\lfloor {\frac{s2}{{K1}}} \right\rfloor m}},$$
(29)
$$\upmu =\frac{\mu maxS\left[1-\left(\frac{S}{Sm}\right)\right]n}{(S+KS)},$$
(30)
$$\mu = \frac{{umaxS\left\lfloor {1 - \frac{{\text{s}}}{{{\text{Sm}}}}} \right\rfloor {\text{n}}}}{{S + Ks[1 - \left\lfloor \frac{s}{Sm} \right\rfloor m}},$$
(31)
$$\upmu =\frac{\mathrm{\mu maxS \, exp}(-\mathrm{ S}/\mathrm{Ki})}{ (\mathrm{S }+\mathrm{ KS})}.$$
(32)

Here, µ = specific growth rate of biomass, µmax = maximum consumption rate constant, S = substrate concentration, K1 = the substrate inhibition constant (mg/L), Ks = Monod constant, and Sm = decisive inhibitor concentration (mg/L); n and m are experimental constants.

For the range of concentrations in the study (5–200 mg/L), the length of the lag phase t0 grew exponentially with the Malachite green concentration Supplementary Fig. 1a. Thus, Malachite green was considered to have an inhibiting effect on microbial development at high concentrations. These findings correspond with those previously recorded for mixed cultures36. We compared the evolution of the lag phase to the particular growth rate to gain advanced insight into the impact of the lag phase. Two trends were observed, one below Supplementary Fig. 1a,c and one above Supplementary Fig. 1b the 100 mg/L Malachite green concentration. The time of the lag phase t0 rose linearly with an increase in the maximum specific growth rate when the concentration of Malachite green Supplementary Fig. 1a was less than 100 mg/L. However, a contrary trend was observed for concentrations greater than 100 mg/L supplementary Fig. 1b, where the length of the lag phase t0 increased as the maximum specific growth rate decreased. The findings of the curve fitting Supplementary Fig. 2 using models such as Monod, Luong, Aiba–Edward, Han, and Levenspiel did not match the experimental results and were excluded. The Luong model provided reasonably acceptable results according to software output and visual examination. The accuracy and statistical analyses of the four kinetic models used in the study revealed that Haldane was the most accurate model, having the minimum root-mean-square error and AICc values and the maximum adjusted R2. Table 10 shows the Af and Bf values. The Af and Bf values for Haldane were significant and closest to 1.0.The results of an F-test indicated that the Haldane model was better than the Aiba–Edward, Han, Levenspiel, and Luongmodels, which were 96.1%,92.2%, 84.43%, and 82.2%, respectively. These results indicate that the Haldane model was superior to the rest. The computed values for the Haldane constants in this work, such as the inhibition constant rate symbolized by the maximal growth rate and half-saturation constant umax, Ks, and Ki, were 1.02 h−1, 70 mg/L, and 70 mg/L.