Graphene Oxide as a Highly Efficient and Reusable Adsorbent for Simultaneous Removal of Parabens: Optimization by Response Surface Methodology, Adsorption Isotherms and Reusability Studies

doi:10.21203/rs.3.rs-5231190/v1

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Graphene Oxide as a Highly Efficient and Reusable Adsorbent for Simultaneous Removal of Parabens: Optimization by Response Surface Methodology, Adsorption Isotherms and Reusability Studies

https://doi.org/10.21203/rs.3.rs-5231190/v1

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published 19 Dec, 2024

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Paraben contamination in aquatic systems, primarily from personal care products, pharmaceuticals and industrial effluents, is an increasing environmental concern due to their widespread use as preservatives. The removal of parabens through conventional wastewater treatment processes are difficult and requires the development of innovative water treatment methods. In this study, graphene oxide nanoflakes were produced by Improved Hummers’ method and their adsorption characteristics were investigated for simultaneous removal of five parabens. Fourier transform infrared spectroscopy, Raman Spectroscopy, X-Ray Powder Diffraction, Scanning Electron Microscope and Transmission Electron Microscope were used and the nanoflakes were successfully characterized. The chromatographic method was developed for the simultaneous quantification of parabens. The process optimization overall removal efficiency of parabens was achieved using Response Surface Methodology by a multiple response function. Nonlinear regression was used to fit the equilibrium data and the Freundlich model described the adsorption isotherm data accurately with R² values between 0.9807 and 0.9957. Factors such as mass of adsorbent, pH of solution and their interaction have the most significant impact on the adsorption process, while contact time parameter shows low significance on the response. The adsorption behaviors of parabens were closely correlated with their hydrophobicity. Along with hydrophobic interactions, other mechanisms such as π–π stacking, hydrogen bonding and electrostatic forces, likely played significant role in the strong adsorption of parabens onto the GO surface. The reusability experiment showed that graphene oxide nanoflakes had a high potential present as a reusable adsorbent for the removal of parabens.

Graphene oxide nanoflakes

Parabens

Adsorption Isotherms

Reusability

The growing use of endocrine disrupting chemicals (EDCs) in industry and domestic life has raised a recent awareness about their detrimental impacts on public health. As implied by their name, they interfere with the endocrine system at trace levels, and cause serious health problems in reproductive, cardiovascular, developmental and neurological systems (Schug et al., 2011). Parabens, a class of the most common EDCs, are widely used as preservatives in pharmaceuticals, personal care products, cosmetics, foods and beverages. There have been many studies pointing to the toxicity of parabens on organisms. In vitro studies have showed that parabens penetrate the skin (Pedersen et al., 2007), bind to estrogenic receptors (Wei et al., 2022), and induce the development of breast cancer (Webber, 2013). Additionally, there is increasing evidence on the associated health effects of parabens such as infertility, developmental and neurological disorders and thyroid problems (Aker et al., 2016; Nishihama et al., 2016; Shi et al., 2023).

Methylparaben (MePB), ethylparaben (EtPB), propylparaben (PrPB), butylparaben (BtPB) and benzylparaben (BePB) are the typical compounds of parabens which have structural differences in solubility, length of alkyl chain and antimicrobial activity (Álvarez et al., 2020). The extension in alkyl chain of parabens favors their antimicrobial activity and stability (Bolujoko et al., 2021). However, longer chain parabens have typically less solubility in water, limiting their applicability to the aqueous phases (Vale et al., 2022). To ensure desired antimicrobial activity without exceeding the legal maximum concentrations, more than one paraben is often used in combination. According to regulations from European Union approved in 2013, the maximum concentration of parabens allowed as cosmetic additives was set to 0.4% for an individual paraben and 0.8% for a mixture of parabens (Nowak et al., 2018). In 2014, the upper concentration limits of PrPB and BtPB were decreased to 0.14% for cosmetic products and addition to that, they were prohibited from use in children products (Wei et al., 2021).

The removal of parabens from water sources is crucial due to their potential toxicity to aquatic organisms and humans. Over the years, various techniques have been developed for efficient removal of organic pollutants including flocculation, coagulation, membrane filtration, electrocoagulation and adsorption. Among them, adsorption has generated interest due to its numerous advantages. These advantages include potential efficiency, high molecular-level selectivity, ease of manipulation, low energy consumption and capability to remove various organic contaminants (Titchou et al., 2021). In the adsorption process, a soluble chemical, known as adsorbate, is separated from a fluid by encountering a solid surface, known as the adsorbent. This process occurs through a complex phenomenon, with several parameters significantly affecting the adsorption efficiency. The chemistry, surface area and porosity of the adsorbent material, the nature of adsorbate, the contact time, and the pH of solution are the main parameters influencing the adsorption process (El-Naas & Alhaija, 2011).

Adsorbent materials with a large surface area and granular structure composed of small pores are advantageous, because these materials provide increased interaction with adsorbate molecules, leading to the improvement in the adsorption capacity. Up to now, a diverse array of adsorbent materials have been developed including organic, inorganic and hybrid structures such as zeolites (Hor et al., 2016; Oliveira et al., 2019), hydrogels (Hu et al., 2018; Karlıdağ et al., 2024; Tu et al., 2017), metal oxides (Kumar et al., 2013; Xu et al., 2010), metal-organic frameworks (MOFs) (Bandosz & Petit, 2011; Han et al., 2019; Zhou et al., 2023) and carbon nanomaterials (Guo et al., 2015). Due to its large surface area, high porosity, abundant functional groups and strong adsorption capabilities, graphene oxide (GO) has attracted tremendous research interest for the removal of methylene blue (Yan et al., 2014), tetracycline antibiotics (Gao et al., 2012), polycyclic aromatic hydrocarbons (PAHs) (Wang et al., 2014) and toxic metals (Li et al., 2012; Reynosa-Martínez et al., 2020; Yang et al., 2014) from aqueous solutions. According to previous studies, GO offers a promising solution for the removal of parabens from water.

In this work, GO was prepared by improved Hummers’ method and characterized by FT-IR, XRD, Raman, SEM and TEM analysis. The purpose of this study was to develop highly efficient method for the simultaneous removal of parabens from aqueous solutions and to elucidate the adsorption characteristics of GO by modelling the adsorption process. The influential parameters of adsorption process were optimized using Box-Behnken experimental design. The adsorption behavior of GO was studied via comparison of Langmuir and Freundlich equations using nonlinear regression approach. The best-fitted model was determined by three different error functions and correlation coefficients.

2.1 Materials and Standard Solutions

Graphite powder, high purity grade (99.9995%), was obtained from Alfa Aesar. The other reagent including hydrogen peroxide (35%, H₂O₂), sulfuric acid (95–98%, H₂SO₄), ortho-phosphoric acid (85%, o-H₃PO₄), hydrochloric acid (37%, HCl), potassium permanganate, methanol and acetonitrile were purchased from Merck. Analytical grade (≥ 99%) standards of methyl paraben, ethyl paraben, propyl paraben, benzyl paraben and butyl paraben were supplied by Sigma Aldrich. The stock standard solutions of parabens were prepared at the concentration of 2,000 mg/L in methanol. The mixed standard solutions of parabens were obtained by diluting with ultrapure water from Healforce Smart RO15 system.

2.2 Instrumentation

A Bruker Tensor 27 Attenuated Total Reflection (ATR) FTIR Spectrometer, with a wavenumber sensitivity of ± 0.01 cm⁻¹ and photometric accuracy of ± 0.1%, was used to determine the chemical structure of GO. The analysis were performed at room temperature over a specific wavenumber range (650–4000 cm⁻¹) to identify the functional groups of the material. Raman spectrum was obtained using inVia Renishaw Raman device employing 532 nm laser beam.

A PANalytical X'Pert Pro Analyzer equipped with a Cu-Kα radiation source operating at 45 kV and 40 mA was used to determine the phase of the GO nanoflakes. The scanning angle 2θ was set between 2° and 90° with a step scan rate of 3°/min. A Zeiss EVO LS10 Scanning Electron Microscope (SEM), operating in beam mode at 20 kV with a secondary electron detector, was used to characterize the morphological structure. The solid sample was coated with gold-palladium in an argon plasma for 45 seconds using a Quorum SC7620 Sputter Coater. TEM image was taken using a JEOL JEM 2100 HRTEM operated at 200 kV.

2.3 Preparation of GO

Improved Hummers’ method was utilized to synthesize graphene oxide nanosheets (Marcano et al., 2010). Briefly, 3.0 g of graphite powder and 9.0 g of potassium permanganate was mixed with the acidic solution containing 360 mL of sulfuric acid and 40 mL of o-phosphoric acid. The mixture was stirred at 50°C for 12 hours and then, cooled down to 25°C. This solution was transferred into the ice containing 3.0 mL of hydrogen peroxide solution. Afterward, the solid part of the final mixture was collected using centrifugation at 6000 rpm for 40 minutes. After centrifugation, the precipitate was washed three times with 5.0 N of hydrochloric acid, ethanol and distilled water. At the final step, GO was dried in an oven at 55°C for 24 hours.

2.4 Quantitative Determination of Parabens

A chromatographic method was developed for the quantitative determination of paraben concentrations using a Shimadzu LC-20A High Performance Liquid Chromatography (HPLC) system. The separation of five parabens was achieved using a Phenomenex brand C18 column (4.6 ×250 mm, 5.0 µm) and isocratic elution was employed using 60:40 mixture of 30 mM ammonium formate in ultrapure water at pH 4.6: Acetonitrile with a flow rate of 1.50 mL/min. The injection volume was 30 µL and total run period was 18.0 min. The wavelength of the detector was adjusted to 254 nm.

The chromatogram of paraben mixture is shown in Fig. 1. The retention times of MePB, EtPB, PrPB, BuPB and BePB were observed as 2.949, 4.497, 7.752, 14.328 and 16.056 minutes. Calibration standards at varied concentrations (2.0–10,000 µg/L) were prepared by the dilution of stock solution with ultrapure water. Limit of detection and quantification values (LOD and LOQ) of the developed method were calculated by 3 and 10 times of (standard deviation)/slope ratios, respectively. The standard deviations were obtained by at least five replicate analysis of the lowest calibration level. The calibration plots were constructed using the known concentrations of standard mixtures against the peak area of each paraben. The slope values of calibration plot (R² ≥ 0.99) were used to obtain LOD and LOQ values. LOD and LOQ values, linear range, R² values and repeatability (%RSD) results of each paraben are listed in Table 1.

Table 1

The analytical performance results of parabens for the developed chromatographic method
Analyte	LOD, µg/L	LOQ, µg/L	%RSD	Linear Range, µg/L	R²
MePB	0.41	1.36	4.54	2.0–5,000	0.9974
EtPB	0.42	1.40	7.45	2.0–5,000	0.9975
PrPB	0.75	2.49	3.43	5.0–5,000	0.9976
BuPB	1.05	3.50	12.0	5.0–5,000	0.9976
BeBP	1.34	4.46	5.73	5.0–5,000	0.9976

2.5 Adsorption Experiments

Batch adsorption experiments were conducted by spiking water medium with different concentrations of paraben standard mixture. Secondly, a certain amount of adsorbent was added into the contaminated water medium and the solution was mixed until reaching the equilibrium state for adsorption. The concentration of parabens was determined by HPLC system, and the following equation (Eq. 1) was used to calculate the adsorption capacity.

$$\:Q=\frac{{(C}_{0}-{C}_{e})}{m}V$$

where $\:{C}_{0}$ and $\:{C}_{e}$ are the concentrations of parabens in solutions at the initial and equilibrium conditions in mg/L, $\:Q$ is the adsorption capacity in mg/g, $\:m$ is the amount of adsorbent (g) and $\:V$ is the volume of solution (L).

2.6 Optimization of Adsorption Process

To achieve maximum removal of parabens, the optimum process conditions were investigated using Response Surface Methodology (RSM) based Box-Behnken design. The design included three levels, coded as -1 (low), 0 (medium) and + 1 (high) with a total of 17 experimental runs performed in a repetitive manner to determine the optimum values of three variables: mass of adsorbent (A), pH of solution (B) and contact time (C). The model is presented in Table 2.

Table 2

Three independent process variables and their levels in Box-Behnken design
Factor	Name	Units	Minimum	Maximum	Coded Low	Coded High	Mean
A	Mass of adsorbent	mg	10.00	100.00	-1 ↔ 10.00	+ 1 ↔ 100.00	55.00
B	pH of solution	pH	2.00	10.00	-1 ↔ 2.00	+ 1 ↔ 10.00	6.00
C	Contact time	min	10.00	120.00	-1 ↔ 10.00	+ 1 ↔ 120.00	65.00

The impact of three independent variables on the removal of parabens was analyzed using a second-order polynomial equation (Eq. 2):

$$\:Y={\beta\:}_{0}+\sum\:_{i=1}^{n}{\beta\:}_{i}{x}_{i}+\sum\:_{i=1}^{n}{\beta\:}_{ii}{x}_{i}^{2}+\sum\:_{i=1}^{n}\sum\:_{j=1}^{n}{\beta\:}_{ij}{x}_{i}{x}_{j}+\epsilon\:$$

In this equation, $\:Y$ is the dependent variable and represents the predicted response; n denotes the number of independent variables; the term $\:\epsilon\:$ accounts for random error; $\:{x}_{i}$ and $\:{x}_{j}$ are the independent variables in coded levels, $\:{\beta\:}_{i}$,$\:\:{\beta\:}_{ii}$, $\:{\beta\:}_{ij}$ refers to the linear, quadratic and interaction effects of variables, respectively. All experiments were conducted in triplicates.

2.7 Desorption Experiments

The adsorbent was separated from the aqueous solution by centrifugation for 10 min at 4,000 rpm and desorption experiments of parabens from the surface of adsorbent were carried out using methanol as the elution solvent. The suspension was ultrasonicated for 10 min and dried in an oven at 50 ̊C for 12 h. The removal percentage was evaluated by Eq. 3.

$$\:RE\:\left(\%\right)=100x\frac{({C}_{0}-{C}_{i})}{{C}_{0}}$$

where $\:{C}_{i}$ (mg/L) refers to the final concentrations of parabens after the adsorption cycle.

2.8 Error Analysis

Nonlinear regression was utilized to find the best-fitting model and the unknown parameters of isotherm models. The Solver function of Microsoft Excel was used for nonlinear regression and the best-fitting model was determined based on the error analysis and correlation coefficient (R²) values. The R² and error values of root mean square (RMSE) and the chi-squared parameter (X²) were calculated using the following expressions (Jain et al., 2019; Vitek & Masini, 2023):

$$\:{R}^{2}=1-\frac{\sum\:{\left({q}_{e,exp}-{q}_{e,pred}\right)}^{2}}{\sum\:{\left({q}_{e,exp}-{q}_{e,mean}\right)}^{2}}$$

$$\:RMSE=\:\sqrt{\frac{\sum\:{\left({q}_{e,exp}-{q}_{e,pred}\right)}^{2}}{p}}$$

$$\:{X}^{2}=\sum\:\frac{{\left({q}_{e,exp}-{q}_{e,pred}\right)}^{2}}{{q}_{e,pred}}$$

where $\:{q}_{e,exp}$ and $\:{q}_{e,pred}$ are the adsorption capacities obtained from experimental and model fitting studies, respectively. p is the sample points and $\:{q}_{e,mean}$ is the mean value of $\:{q}_{e,exp}$. A value of the $\:{R}^{2}$ close to 1 suggests a good fit and explains how well the model explains the variability of the data.

3.1 Characterization

FTIR, Raman, XRD, SEM and TEM techniques were used for the structural and morphological characterization of GO and the results are given in Fig. 2. As seen in FTIR spectrum (Fig. 2a), the structure of GO possessed a broad peak at 3171 cm^− 1 corresponding to O-H stretching vibrations, indicating the presence of hydroxyl groups, which contribute to its hydrophilicity. The strong peak at 1740 cm^− 1 was assigned to the C = O stretching vibration of carbonyl groups, including carboxyl or ester functionalities, formed due to the oxidative treatment during synthesis. The peaks at 1565 cm^− 1, 1368 cm^− 1 and 1217 cm^− 1 were attributed to the C = C stretching of the graphitic backbone, C–O stretching and C–O–C stretching of epoxides, respectively. These peaks confirmed that GO was highly functionalized with oxygen-containing groups, which enhance its ability to interact with various molecules while preserving parts of the original graphitic nature of graphene (Ebrahimi Naghani et al., 2023).

In the Raman spectrum of GO (Fig. 2b), the bands at 1351 cm^− 1 and 1588 cm^− 1 corresponded to the D-band and the G-band, respectively. The D-band was associated with structural defects of GO, such as vacancies, edges or functional groups, introduced during the oxidation process. The G-band represented the in-plane stretching of sp² atoms, reflecting the graphitic nature of material. The intensity ratio of the D to G band was calculated as 0.97, implying a considerable amount of defects with the retention of sp² hybrid domains. The broad band at 2697 cm^− 1 was the indicative of the disruption of the π-π stacking and consistent with the functionalization and oxidation of the graphene sheets (Aujara et al., 2019). This was also confirmed with the XRD pattern (Fig. 2c). The single peak observed at around 10° corresponding to the (001) diffraction plane of GO was a result of the regular stacking of GO layers due to the incorporation of the oxygen-containing groups between the layers (Jiao et al., 2017). SEM and TEM images (Fig. 2d-e) showed the flake-like wrinkled surface and stacked layered structure of GO, which was in line with previous literature (Gurunathan et al., 2016).

3.2 Model Determination and Statistical Analysis

The optimization of key variables influencing the adsorption performance was employed using Box-Behnken Design approach. Mass of adsorbent, pH of solution and contact time were identified as the primary input variables that significantly affect the uptake of parabens. Both individual and interactive effects of these factors on the adsorption capacity of GO were evaluated through design of experiments. A quadratic model, suggested by the Design Expert software, was selected to describe the adsorption of parabens onto GO adsorbent. A multiple response function (MRF) was generated for the simultaneous optimization of multiple responses, with the removal percentages of five parabens considered as five responses in the design. The formula for the MRF in the first run, used as an example is provided below (Eq. 7) (Bezerra et al., 2019):

$$\:MR{F}_{1}=\frac{{Y}_{i,1}}{{Y}_{i,max}}+\frac{{Y}_{j,1}}{{Y}_{j,max}}+\frac{{Y}_{k,1}}{{Y}_{k,max}}+\frac{{Y}_{l,1}}{{Y}_{l,max}}+\frac{{Y}_{m,1}}{{Y}_{m,max}}$$

In this equation, Y is the removal percentage, the terms i, j, .., m correspond to each paraben, the “max” term indicates the highest value observed across all run, and the number “1” refers to the specific run in the sequence (in this case, the first run). The final model in actual (uncoded) terms is given in the following equation:

$$\:Y=4.55973+0.019198A-0.156323B+0.000134C+0.001551AB+0.000226BC-0.000162{A}^{2}$$

The positive and negative signs in front of the terms in Eq. (4) indicate whether they have a synergistic or antagonistic effect on the response. Mass of adsorbent (A), contact time (C) and interaction terms (AB and BC) affect the response positively, while pH of solution (B) and the quadratic term of adsorbent mass (A²) have negative influence on the response function.

To evaluate the adequacy of the model and examine the relationships between the parameters and responses, Analysis of Variance (ANOVA) test was applied. The F-values and p-values were used to assess the significance of both the overall model and its individual terms. Table 3 shows a summary of the ANOVA results for the adsorption of parabens onto GO. As seen in Table 2, high F-value for the model (118.84) with a p-value of less than 0.0001 indicates that the model is statistically significant and proves that the model can reliably predict the response. The model fits well with a high R² value of 0.9862, and no significant lack of fit (p = 0.0514), confirming its adequacy for predicting the adsorption behavior accurately. Additionally, the predicted R² value of 0.9249 implies the strong prediction accuracy of the model for future applications.

Table 3

ANOVA results of the second order polynomial equation generated for the prediction of parabens removal efficiency
Source	Sum of Squares	df	Mean	F-value	p-value	Remark
Model	3.10	6	0.5170	118.84	< 0.0001	significant
A	1.87	1	1.87	429.18	< 0.0001
B	0.4064	1	0.4064	93.42	< 0.0001
C	0.0535	1	0.0535	12.30	0.0057
AB	0.3118	1	0.3118	71.68	< 0.0001
BC	0.0098	1	0.0098	2.26	0.1634
A²	0.4532	1	0.4532	104.18	< 0.0001
Residual	0.0435	10	0.0044
Lack of Fit	0.0392	6	0.0065	6.06	0.051409	not significant
Pure Error	0.0043	4	0.0011
Cor Total	3.15	16
R²	0.9862
Adjusted R²	0.9779
Predicted R²	0.9249
Adeq Precision	37.1510

The model is graphically illustrated through 3D response surface graphs in Fig. 3. Factors such as mass of adsorbent, pH of solution and their interaction have the most significant impact on the adsorption process, while contact time parameter shows low significance on the response. The adsorption of parabens decreased across the pH range of 2 to 10. Considering the pKa values of parabens (8.5–8.2), the higher adsorption at lower pH values can be attributed to their neutral form at these conditions, which promotes adsorption by reducing electrostatic repulsion between the paraben molecules and negatively charged surface of GO. As the pH increases, the fraction of negatively charged paraben species increases, resulting in greater electrostatic repulsion with the negatively charged surfaces of the adsorbent, thus making adsorption less favorable. This behavior is consistent with the adsorption mechanisms observed for various organic compounds (Al-Ghouti et al., 2022; Pei et al., 2013).

As another significant parameter, the optimum mass of adsorbent was also investigated by varying its value between 10–100 mg. As expected, the increase in adsorbent mass resulted in higher adsorption capacity due to the greater availability of adsorption sites on the adsorbent surface. This enhances the interaction between the adsorbent and paraben molecules, improving the overall removal efficiency. However, beyond 60 mg adsorbent mass, a plateau was observed, indicating little additional adsorption despite the increased mass. The third variable influencing the adsorption process was the contact time. In this study, there was a slight increase in removal efficiency with the increase of contact time. This may be likely due to the rapid occupation of active sites in the initial stage, which causes the adsorption rate to slow down as the sites become increasingly saturated. Finally, as a result of the experimental design, the optimal values for the three variables were determined to be an adsorbent mass of 60 mg, a solution pH of 2.0, and a contact time of 30 minutes.

3.3 Adsorption Isotherms

The adsorption equilibrium data are effectively depicted by adsorption isotherms, which describe the relationship between the amount of solute adsorbed per unit mass of adsorbent (q_e) and the equilibrium concentration of the solute in the solution (C_e). The adsorption isotherms for the removal of five parabens (MePB, EtPB, PrPB, BuPB and BePB) were studied using initial concentration of parabens between 2.5 mg/L and 80 mg/L at an adsorbent mass level of 60 mg. The adsorption equilibrium data were obtained under optimum process conditions and fitted to Langmuir and Freundlich adsorption isotherms for further investigation.

The Langmuir model assumes a monolayer adsorption on a homogeneous surface, while the Freundlich model describes adsorption on heterogeneous surface where the adsorption energy decreases exponentially with increasing coverage. The Langmuir and Freundlich models can be expressed by the following equations, respectively (Jain et al., 2019):

$$\:{q}_{e}=\frac{{q}_{m}{K}_{L}{C}_{e}}{1+{K}_{L}{C}_{e}}$$

$$\:{q}_{e}={K}_{F}{\left({C}_{e}\right)}^{\frac{1}{n}}$$

where $\:{q}_{e}$ is the amount of adsorbed paraben per unit mass of adsorbent at the equilibrium (mg/g), $\:{C}_{e}$ is the equilibrium concentration of paraben (mg/L), n is the heterogeneity factor of Freundlich model, $\:{K}_{L}$ (L/mg) and $\:{K}_{F}$ (dimensionless) are the model constants.

The equilibrium data were analyzed using nonlinear regression to determine the R² values and isotherm constants. The results are shown in Table 4. The R² values calculated for Freundlich model (ranging from 0.9807 to 0.9957) were found to be significantly closer to unity when compared to Langmuir model (ranging from 0.9550 to 0.9878) for all compounds. The fitted model results with the experimental data are shown in Fig. 4. All obtained values, including the closeness of experimental and model values, higher R² values and lower error values confirm the better fit of the Freundlich model to the isotherm data in this study. As given in Table 4, the n values were calculated to be greater than 1, indicating the favorable adsorption of parabens. In addition, the constants of Langmuir and Freundlich models (K_L and K_F) increased with increasing molecular weight and length of the alkyl side chain. The adsorption behaviors of parabens were closely correlated with their hydrophobicity. Along with hydrophobic interactions, other mechanisms such as π–π stacking, hydrogen bonding and electrostatic forces, likely played significant role in the strong adsorption of parabens onto the GO surface. Possible adsorption mechanism of parabens onto GO nanoflakes was presented in Fig. 5.

Table 4

Isotherm parameters for removal of parabens using graphene oxide
Isotherm Models	Parameter	Compounds
Isotherm Models	Parameter	MePB	EtPB	PrPB	BuPB	BePB
Langmuir	q_m (mg/g)	9.3971	10.3382	10.8573	11.9278	14.1634
	K_L (L/mg)	0.1742	0.1884	0.3036	0.5266	3.3042
	R²	0.9550	0.9521	0.9600	0.9684	0.9878
	RMSE	0.5205	0.5859	0.5866	0.5807	0.4221
	X²	5.9372	4.3226	3.7890	3.6975	2.1566
Freundlich	n	2.1719	2.0909	2.2186	2.3221	2.3860
	K_F (mg/g)(L/mg)^(1/n)	1.7615	1.9452	2.5691	3.5792	8.7487
	R²	0.9957	0.9945	0.9940	0.9910	0.9807
	RMSE	0.1614	0.1977	0.2275	0.3101	0.5297
	X²	0.1290	0.1500	0.2326	0.4011	0.9485

3.4 Reusability Study

The desorption and reusability properties of an adsorbent are crucial for its practical and economical application in environmental processes. The regeneration of adsorbents through effective desorption reduces the environmental impact by limiting the waste generation. Herein, the reusability of GO nanoflakes was studied for five cycles and the results were shown in Fig. 6. The removal efficiency of GO remained constant during five consecutive adsorption/desorption cycles, demonstrating its excellent stability and potential for reuse. This indicated that GO can be effectively regenerated, maintaining its adsorption capacity without significant loss of performance.

The GO nanoflakes were synthesized using Improved Hummers method and then utilized as an adsorbent for the simultaneous removal of five parabens. The chemical and morphological characteristics of GO nanoflakes were identified by FT-IR, Raman, XRD, SEM and TEM results. The statistical design experiment by RSM was used for the optimization of adsorption process and batch adsorption experiments were carried out under optimum process parameters. The results indicated that GO nanoflakes had a very high adsorption capacity at pH 2.0. The high removal rate obtained after multiple cycles highlighted the structural integrity of GO, suggesting that the material did not undergo degradation or substantial changes in surface properties during the adsorption/desorption cycles, making it a cost-effective and environmentally sustainable option for long-term use in the removal of parabens.

Declaration of Competing Interest

The author declares that she has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author Contribution

E.O.E conducted experiments, metholodology, formal analysis, wrote the main text, reviewed the manuscript.

Acknowledgements

The author would like to thank İstanbul Technical University Scientific Research Projects Coordination Unit for the project supported in Project Number: MGA-2023-44569.

Availability of Data

Data will be available on reasonable request.

Aker, A. M., Watkins, D. J., Johns, L. E., Ferguson, K. K., Soldin, O. P., Del Toro, L. V. A., Alshawabkeh, A. N., Cordero, J. F., & Meeker, J. D. (2016). Phenols and parabens in relation to reproductive and thyroid hormones in pregnant women. Environmental research, 151, 30-37.
Al-Ghouti, M. A., Sayma, J., Munira, N., Mohamed, D., Da’na, D. A., Qiblawey, H., & Alkhouzaam, A. (2022). Effective removal of phenol from wastewater using a hybrid process of graphene oxide adsorption and UV-irradiation. Environmental Technology & Innovation, 27, 102525. https://doi.org/https://doi.org/10.1016/j.eti.2022.102525
Álvarez, M. A., Ruidíaz-Martínez, M., Cruz-Quesada, G., López-Ramón, M. V., Rivera-Utrilla, J., Sánchez-Polo, M., & Mota, A. J. (2020). Removal of parabens from water by UV-driven advanced oxidation processes. Chemical Engineering Journal, 379, 122334. https://doi.org/https://doi.org/10.1016/j.cej.2019.122334
Aujara, K. M., Chieng, B. W., Ibrahim, N. A., Zainuddin, N., & Thevy Ratnam, C. (2019). Gamma-Irradiation Induced Functionalization of Graphene Oxide with Organosilanes. International Journal of Molecular Sciences, 20(8), 1910. https://www.mdpi.com/1422-0067/20/8/1910
Bandosz, T. J., & Petit, C. (2011). MOF/graphite oxide hybrid materials: exploring the new concept of adsorbents and catalysts. Adsorption, 17, 5-16.
Bezerra, M. A., Ferreira, S. L. C., Novaes, C. G., dos Santos, A. M. P., Valasques, G. S., da Mata Cerqueira, U. M. F., & dos Santos Alves, J. P. (2019). Simultaneous optimization of multiple responses and its application in Analytical Chemistry – A review. Talanta, 194, 941-959. https://doi.org/https://doi.org/10.1016/j.talanta.2018.10.088
Bolujoko, N. B., Unuabonah, E. I., Alfred, M. O., Ogunlaja, A., Ogunlaja, O. O., Omorogie, M. O., & Olukanni, O. D. (2021). Toxicity and removal of parabens from water: A critical review. Science of The Total Environment, 792, 148092. https://doi.org/https://doi.org/10.1016/j.scitotenv.2021.148092
Ebrahimi Naghani, M., Neghabi, M., Zadsar, M., & Abbastabar Ahangar, H. (2023). Synthesis and characterization of linear/nonlinear optical properties of graphene oxide and reduced graphene oxide-based zinc oxide nanocomposite. Scientific Reports, 13(1), 1496. https://doi.org/10.1038/s41598-023-28307-7
El-Naas, M. H., & Alhaija, M. A. (2011). Modelling of adsorption processes. Mathematical Modelling, 579-600.
Gao, Y., Li, Y., Zhang, L., Huang, H., Hu, J., Shah, S. M., & Su, X. (2012). Adsorption and removal of tetracycline antibiotics from aqueous solution by graphene oxide. Journal of colloid and interface science, 368(1), 540-546.
Guo, H., Jiao, T., Zhang, Q., Guo, W., Peng, Q., & Yan, X. (2015). Preparation of graphene oxide-based hydrogels as efficient dye adsorbents for wastewater treatment. Nanoscale research letters, 10, 1-10.
Gurunathan, S., Hyun Park, J., Choi, Y.-J., Woong Han, J., & Kim, J.-H. (2016). Synthesis of graphene oxide-silver nanoparticle nanocomposites: an efficient novel antibacterial agent. Current Nanoscience, 12(6), 762-773.
Han, Q., Wang, Z., Chen, X., Jiao, C., Li, H., & Yu, R. (2019). Facile synthesis of Fe-based MOFs (Fe-BTC) as efficient adsorbent for water purifications. Chemical Research in Chinese Universities, 35(4), 564-569.
Hor, K. Y., Chee, J. M. C., Chong, M. N., Jin, B., Saint, C., Poh, P. E., & Aryal, R. (2016). Evaluation of physicochemical methods in enhancing the adsorption performance of natural zeolite as low-cost adsorbent of methylene blue dye from wastewater. Journal of Cleaner Production, 118, 197-209.
Hu, X.-S., Liang, R., & Sun, G. (2018). Super-adsorbent hydrogel for removal of methylene blue dye from aqueous solution. Journal of Materials Chemistry A, 6(36), 17612-17624.
Jain, S. N., Shaikh, Z., Mane, V. S., Vishnoi, S., Mawal, V. N., Patel, O. R., Bhandari, P. S., & Gaikwad, M. S. (2019). Nonlinear regression approach for acid dye remediation using activated adsorbent: Kinetic, isotherm, thermodynamic and reusability studies. Microchemical Journal, 148, 605-615. https://doi.org/https://doi.org/10.1016/j.microc.2019.05.024
Jiao, X., Qiu, Y., Zhang, L., & Zhang, X. (2017). Comparison of the characteristic properties of reduced graphene oxides synthesized from natural graphites with different graphitization degrees. RSC advances, 7(82), 52337-52344.
Karlıdağ, N. E., Göver, T., Er, E. Ö., Bozyiğit, G. D., Turak, F., & Bakırdere, S. (2024). Development of a novel treatment strategy for the removal of cadmium from wastewater samples using poly (vinyl alcohol)-based magnetic hydrogel beads. Journal of Nanoparticle Research, 26(6), 1-14.
Kumar, K. Y., Muralidhara, H., Nayaka, Y. A., Balasubramanyam, J., & Hanumanthappa, H. (2013). Low-cost synthesis of metal oxide nanoparticles and their application in adsorption of commercial dye and heavy metal ion in aqueous solution. Powder technology, 246, 125-136.
Li, Z., Chen, F., Yuan, L., Liu, Y., Zhao, Y., Chai, Z., & Shi, W. (2012). Uranium (VI) adsorption on graphene oxide nanosheets from aqueous solutions. Chemical engineering journal, 210, 539-546.
Marcano, D. C., Kosynkin, D. V., Berlin, J. M., Sinitskii, A., Sun, Z., Slesarev, A., Alemany, L. B., Lu, W., & Tour, J. M. (2010). Improved synthesis of graphene oxide. ACS nano, 4(8), 4806-4814.
Nishihama, Y., Yoshinaga, J., Iida, A., Konishi, S., Imai, H., Yoneyama, M., Nakajima, D., & Shiraishi, H. (2016). Association between paraben exposure and menstrual cycle in female university students in Japan. Reproductive Toxicology, 63, 107-113.
Nowak, K., Ratajczak–Wrona, W., Górska, M., & Jabłońska, E. (2018). Parabens and their effects on the endocrine system. Molecular and Cellular Endocrinology, 474, 238-251. https://doi.org/https://doi.org/10.1016/j.mce.2018.03.014
Oliveira, J. A., Cunha, F. A., & Ruotolo, L. A. (2019). Synthesis of zeolite from sugarcane bagasse fly ash and its application as a low-cost adsorbent to remove heavy metals. Journal of Cleaner Production, 229, 956-963.
Pedersen, S., Marra, F., Nicoli, S., & Santi, P. (2007). In vitro skin permeation and retention of parabens from cosmetic formulations. International journal of cosmetic science, 29(5), 361-367.
Pei, Z., Li, L., Sun, L., Zhang, S., Shan, X.-q., Yang, S., & Wen, B. (2013). Adsorption characteristics of 1,2,4-trichlorobenzene, 2,4,6-trichlorophenol, 2-naphthol and naphthalene on graphene and graphene oxide. Carbon, 51, 156-163. https://doi.org/https://doi.org/10.1016/j.carbon.2012.08.024
Reynosa-Martínez, A., Tovar, G. N., Gallegos, W., Rodríguez-Meléndez, H., Torres-Cadena, R., Mondragón-Solórzano, G., Barroso-Flores, J., Alvarez-Lemus, M., Montalvo, V. G., & López-Honorato, E. (2020). Effect of the degree of oxidation of graphene oxide on As (III) adsorption. Journal of Hazardous Materials, 384, 121440.
Schug, T. T., Janesick, A., Blumberg, B., & Heindel, J. J. (2011). Endocrine disrupting chemicals and disease susceptibility. The Journal of steroid biochemistry and molecular biology, 127(3-5), 204-215.
Shi, Y., Wang, H., Zhu, Z., Ye, Q., Lin, F., & Cai, G. (2023). Association between exposure to phenols and parabens and cognitive function in older adults in the United States: A cross-sectional study. Science of The Total Environment, 858, 160129.
Titchou, F. E., Zazou, H., Afanga, H., El Gaayda, J., Akbour, R. A., & Hamdani, M. (2021). Removal of Persistent Organic Pollutants (POPs) from water and wastewater by adsorption and electrocoagulation process. Groundwater for Sustainable Development, 13, 100575. https://doi.org/https://doi.org/10.1016/j.gsd.2021.100575
Tu, H., Yu, Y., Chen, J., Shi, X., Zhou, J., Deng, H., & Du, Y. (2017). Highly cost-effective and high-strength hydrogels as dye adsorbents from natural polymers: chitosan and cellulose. Polymer Chemistry, 8(19), 2913-2921.
Vale, F., Sousa, C. A., Sousa, H., Santos, L., & Simões, M. (2022). Parabens as emerging contaminants: Environmental persistence, current practices and treatment processes. Journal of Cleaner Production, 347, 131244. https://doi.org/https://doi.org/10.1016/j.jclepro.2022.131244
Vitek, R., & Masini, J. C. (2023). Nonlinear regression for treating adsorption isotherm data to characterize new sorbents: Advantages over linearization demonstrated with simulated and experimental data. Heliyon, 9(4). https://doi.org/10.1016/j.heliyon.2023.e15128
Wang, J., Chen, Z., & Chen, B. (2014). Adsorption of polycyclic aromatic hydrocarbons by graphene and graphene oxide nanosheets. Environmental science & technology, 48(9), 4817-4825.
Webber, K. E. (2013). Studies on the effects of paraben mixtures on MCF-7 breast cancer cells in culture.
Wei, F., Cheng, H., & Sang, N. (2022). Comprehensive assessment of estrogenic activities of parabens by in silico approach and in vitro assays. Science of The Total Environment, 845, 157194. https://doi.org/https://doi.org/10.1016/j.scitotenv.2022.157194
Wei, F., Mortimer, M., Cheng, H., Sang, N., & Guo, L.-H. (2021). Parabens as chemicals of emerging concern in the environment and humans: A review. Science of The Total Environment, 778, 146150. https://doi.org/https://doi.org/10.1016/j.scitotenv.2021.146150
Xu, B., Wu, F., Zhao, X., & Liao, H. (2010). Benzotriazole removal from water by Zn–Al–O binary metal oxide adsorbent: Behavior, kinetics and mechanism. Journal of Hazardous Materials, 184(1-3), 147-155.
Yan, H., Tao, X., Yang, Z., Li, K., Yang, H., Li, A., & Cheng, R. (2014). Effects of the oxidation degree of graphene oxide on the adsorption of methylene blue. Journal of Hazardous Materials, 268, 191-198.
Yang, S., Li, L., Pei, Z., Li, C., Lv, J., Xie, J., Wen, B., & Zhang, S. (2014). Adsorption kinetics, isotherms and thermodynamics of Cr (III) on graphene oxide. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 457, 100-106.
Zhou, D.-D., Liu, Q.-Y., Chen, M., Cao, Y.-W., Zhuang, L.-Y., Yang, Z.-H., & Xu, Z. (2023). The synthesis, application and mechanism of a novel Zr-based magnetic MOFs adsorption material. Journal of Environmental Chemical Engineering, 11(3), 109666.

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Graphene Oxide as a Highly Efficient and Reusable Adsorbent for Simultaneous Removal of Parabens: Optimization by Response Surface Methodology, Adsorption Isotherms and Reusability Studies

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Journal Publication

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Abstract

Figures

1. Introduction

2. Experimental

2.1 Materials and Standard Solutions

2.2 Instrumentation

2.3 Preparation of GO

2.4 Quantitative Determination of Parabens

2.5 Adsorption Experiments

2.6 Optimization of Adsorption Process

2.7 Desorption Experiments

2.8 Error Analysis

3. Results and Discussion

3.1 Characterization

3.2 Model Determination and Statistical Analysis

3.3 Adsorption Isotherms

3.4 Reusability Study

4. Conclusions

Declarations

Declaration of Competing Interest

Author Contribution

Acknowledgements

Availability of Data

References

Additional Declarations

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