Optimization and modeling of Pb 2+ ions adsorption on bentonite and HNO 3 -activated bentonite using Taguchi and ANOVA methods

Abstract

This study investigates the activation of bentonite using HNO₃ and its application for Pb²⁺ adsorption. Physical-chemical analyses (Brunauer–Emmett–Teller, X-ray diffraction, and Fourier transform infrared) revealed that HNO₃ activation enhanced the surface area and altered the structural characteristics of bentonite. Adsorption experiments, designed using the Taguchi method (L25, four factors, five levels), identified contact time as the most influential factor for Pb²⁺ removal. The maximum Pb²⁺ adsorption capacity of HNO₃-activated bentonite was 1.3 times higher than raw bentonite (61.77 mg/g vs 48.22 mg/g). Adsorption isotherms fitted well with the Temkin model (R² > 0.9, χ² < 0.15), and kinetic data followed the Elovich model (R² > 0.91, χ² < 1.44). The initial sorption rate (α) for HNO₃-activated bentonite was 1.12 times greater than that of bentonite, indicating enhanced adsorption kinetics. This study demonstrates the effectiveness of HNO₃-activated bentonite for efficient Pb²⁺ removal from aqueous solutions.

Keywords

Adsorption bentonite Taguchi ANOVA

Highlights

HNO₃ activation enhanced bentonite's surface area and structural characteristics, confirmed by Brunauer–Emmett–Teller, X-ray diffraction, and Fourier transform infrared analyses.

The Taguchi method (L25) identified contact time as the key factor affecting Pb²⁺ adsorption for both types of bentonite.

HNO₃-activated bentonite had a Pb²⁺ adsorption capacity (q_max) of 61.77 mg/g, 1.3 times higher than natural bentonite's 48.22 mg/g.

Adsorption isotherm data fit the Temkin model (R² > 0.9, χ² < 0.15), while kinetic analysis matched the Elovich model (R² > 0.91, χ² < 1.44).

The initial sorption rate (α) for HNO₃-activated bentonite was 1.12 times greater than that of natural bentonite, indicating improved adsorption efficiency for Pb²⁺ ions.

Introduction

As production increases to meet rising societal demands, it leads to various environmental pollution challenges that impact human health and ecosystems. Notably, water pollution from heavy metal-contaminated wastewater poses serious risks to aquatic life and human health, necessitating urgent attention and effective mitigation strategies (Aziz et al., 2023). Heavy metals like lead (Pb²⁺) are notably toxic and can cause severe health problems even in minimal concentrations (Fu and Wang, 2011). According to the U.S. Environmental Protection Agency, a list of 129 metal and nonmetal pollutants is frequently discharged into the environment, with the top five metal pollutants being copper (Cu), zinc (Zn), chromium (Cr), lead (Pb), and nickel (Ni). Of these, Pb²⁺ is especially dangerous, posing severe risks to both human health and ecological systems (Fu and Wang, 2011).

The pollution of Pb²⁺ can originate from multiple sources, including the application of pesticides and fertilizers, contaminated irrigation water, soil polluted with heavy metals, and leachate from landfills (Bouida et al., 2022). Exposure to Pb²⁺ can cause extensive damage to various organs and systems in the body, including the kidneys, liver, reproductive system, central nervous system, and brain function (Collin et al., 2022). Thus, removing toxic heavy metals, particularly Pb²⁺, from water and wastewater is crucial for protecting both human health and the environment (Fu and Wang, 2011).

Traditional methods for removing heavy metals, such as Pb²⁺, from aqueous solutions are well-established and widely utilized. These techniques typically include chemical precipitation (Kurniawan et al., 2006), coagulation (Tang et al., 2016), ion exchange (Dabrowski et al., 2004), solvent extraction (Jha et al., 2012), filtration (Abdulraheem et al., 2020), and membrane technologies (Birniwa et al., 2023). Despite their effectiveness, these methods often come with limitations such as high costs, complex operations, and potential byproducts (Panayotova and Velikov, 2003; Zhu et al., 2019). In contrast, adsorption technology has garnered increasing attention due to its efficiency, cost-effectiveness, and environmental benefits (Rashid et al., 2021). Adsorption is favored for its ability to remove contaminants without generating byproducts (Park et al., 2019). However, the effectiveness of adsorption can be influenced by factors such as solute concentration, the ratio of adsorbent to adsorbate, contact time, temperature, the type and modification of the adsorbent, ionic strength, and pH (Wang et al., 2016; Zhang et al., 2015). Achieving optimal pollutant removal necessitates careful tuning of these parameters to enhance the efficiency and effectiveness of adsorption processes (Razmi and Ghasemi-Fasaei, 2018).

In recent years, the Taguchi method has gained prominence for its ability to utilize orthogonal arrays (OAs) to efficiently examine multiple variables with a minimal number of experiments. This approach is particularly advantageous compared to traditional design methods, which often require a full factorial design that can involve a prohibitively large number of trials (Türkmen et al., 2008). The Taguchi method identifies the best combination of process parameters to reduce costs, enhance quality, and increase efficiency (Singh et al., 2019). Additionally, to validate the results, the analysis of variance (ANOVA) method was utilized. ANOVA helps quantify the percentage contribution of each process parameter to the desired outcomes, ensuring a thorough understanding of the influencing factors in the adsorption process (Mustapha et al., 2021). The Taguchi method, in conjunction with ANOVA, has been successfully employed to optimize the removal of various pollutants. For example, it has been used for Cu²⁺ removal with geopolymer materials synthesized from different fly ashes (Litu et al., 2024), for Pb²⁺ removal using nanohydroxyapatite (Googerdchian et al., 2018), and for Pb²⁺ removal by magnetized nanoparticles derived from potato peel (Jalees, 2020). This combination not only enhances the research process but also improves the reliability of the findings (Madan and Wasewar, 2017).

Adsorbents based on natural clays, including kaolin, bentonite, and zeolites, have been extensively employed to remove heavy metal pollutants in wastewater (Gu et al., 2019; Kinoti et al., 2022). Bentonite, in particular, stands out due to its inherent properties that enhance its effectiveness in removing cationic heavy metals from water. The clay's net negative surface charge enhances strong electrostatic interactions with positively charged metal ions, leading to efficient removal (Toor and Jin, 2012). This clay is highly valued in water and wastewater treatment for several reasons: its ease of application, low cost, and widespread availability make it an economically viable option (Anna et al., 2015; Chen et al., 2011; Prabhu and Prabhu, 2018). Additionally, the micron-sized particles of bentonite offer a significant external and internal surface area, enhancing its adsorption capacity for a wide range of heavy metal contaminants in water (Zhu et al., 2016).

Bentonite, a clay mineral primarily composed of montmorillonite, is widely available and has demonstrated effectiveness in treating metal-contaminated wastewater. Its benefits include broad availability, natural abundance, stability in both physical and chemical properties, environmental friendliness, and strong adsorption capacity (Alexander et al., 2019; Pei et al., 2021). To further enhance its adsorption capabilities, ongoing research focuses on surface modifications of bentonite. Various techniques have been explored to improve its performance, such as activation with cationic surfactants, clay–rubber mixtures, polymer addition, coupling of inorganic and organic anions, and grafting of organic compounds (Alexander et al., 2019; Borah et al., 2022). Fe₃O₄ has also been explored as an effective activation agent for bentonite. It has been successfully utilized in both batch experiments (Ewis et al., 2020) and fixed-bed columns (Sarran et al., 2024) for adsorbing gasoline emulsion. The magnetic properties of Fe₃O₄ facilitate easy recovery of the activated bentonite, further improving the feasibility of its use in large-scale water treatment processes (Sarran et al., 2024).

Notably, acid activation of natural bentonite has been widely studied (Al-Essa, 2018; Ardelia et al., 2024; Bayram et al., 2021; Carrado and Komadel, 2009; Kul and Koyuncu, 2010; Rezapour et al., 2016; Ullah et al., 2016). Acidic treatment improves bentonite's performance by removing impurities and exchangeable cations from the structure, leading to enhanced properties such as increased surface area and pore volume, making it more effective for applications like pollutant removal (Amari et al., 2010). Multiple studies, such as those by Liu (2007), Noyan et al. (2007), and Vimonses et al. (2009), highlight the positive effects of acid activation on bentonite, improving its physical characteristics for adsorption purposes and other industrial uses. For example, acid-activated bentonite (ABC) achieved significantly higher removal efficiencies for Fe²⁺, Zn²⁺, and Ni²⁺ from pharmaceutical effluent compared to non-activated bentonite (NBC). ABC removed up to 89.9% of Zn²⁺, 81.8% of Fe²⁺, and 75.5% of Ni²⁺, outperforming NBC's lower removal rates. The acid treatment also increased the surface area of bentonite from 46 to 76 m²/g and expanded its pore diameter (Ibigbami et al., 2022).

However, while activation with acids like H₂SO₄ (Ardelia et al., 2024; Bayram et al., 2021; Rezapour et al., 2016) and HCl (Al-Essa, 2018; Kul and Koyuncu, 2010; Ullah et al., 2016) has been widely studied, the use of nitric acid (HNO₃) for activation remains relatively underexplored. Only a few studies (Ibigbami et al., 2022) have addressed this process, and even fewer have investigated the application of HNO₃-activated bentonite for Pb²⁺ removal. This gap is directly addressed by this study, which evaluates the efficacy of HNO₃-activated bentonite for Pb²⁺ adsorption and compares it to raw bentonite's adsorption capacity. Additionally, this study pioneers the use of the Taguchi method in combination with ANOVA to optimize critical adsorption conditions such as reaction time, pH, Pb²⁺ concentration, and material mass, while also assessing the relative contributions of each parameter. The results not only underscore the distinct effects of HNO₃ activation but also offer valuable insights into optimizing Pb²⁺ removal efficiency, presenting new possibilities for more effective environmental remediation strategies.

Materials and methods

Materials and chemicals

The natural raw bentonite used in this study was purchased from Damao Chemical Reagent Factory (Tianjin, China), with a purity of ≥ 98%. For the activation of bentonite, analytical grade nitric acid (HNO₃) was used, with a purity of ≥ 99%. Additionally, all other chemicals required for the experiments were of analytical reagent (AR) grade from Merck, with purities of ≥ 99%. All chemicals were utilized as received, without further purification.

Preparation of HNO₃-activated bentonite

Raw bentonite was transformed into HNO₃-activated bentonite through a series of steps, as described in the previous study (Ibigbami et al., 2022). The process began by treating the raw bentonite with a 2 M nitric acid (HNO₃) solution at a mass-to-volume ratio of 1 g to 2 mL. The solution was stirred at a speed of 300 r/min during the 4-h duration in the water bath, with the temperature controlled at 70 ± 1 °C. To prevent evaporation, the mixture was stirred in a closed container, which helped maintain the required temperature and prevent the loss of solvent during the process. Once the activation process was complete, the HNO₃-activated bentonite was dried in an oven. After drying, it was ground into a fine powder and sieved through a 100 μm mesh to obtain a uniform particle size for further analysis and experimentation.

Characterization methods

The mineralogy, functional groups and surface properties were characterized using X-ray diffraction (XRD), Fourier transform infrared (FTIR), and Brunauer–Emmett–Teller (BET) respectively. The XRD data of the adsorbents were collected with a PANalytical EMPYREAN instrument using Cu-Kα radiation. The FTIR spectra were recorded with a PerkinElmer Spectrum 10.5.2 spectrophotometer. Surface area measurements were performed on Nova Station A (Quantachrome Instruments version 11.0, Miami, FL, USA) surface area and porosity analyzer.

Batch adsorption experiments

Stock solutions of 1000 mg/L Pb were prepared using Pb(NO₃)₂ as source, dissolved in deionized water. The initial pH of the Pb²⁺ ion solutions was adjusted by adding appropriate amounts of diluted HNO₃ or NaOH to achieve the desired levels. For each experiment, a specific quantity of adsorbent was introduced into the Pb²⁺ solutions at room temperature (25 ± 2 °C).

Batch adsorption tests were performed in triplicate using polyethylene bottles with a total volume of 50 mL. The bottles were shaken in a shaking incubator (LSI-3016R, LabTech, Korea) at 3000 r/min to ensure thorough mixing and optimal contact between the adsorbent and the Pb ions. After the designated contact time, the supernatant liquid was filtered using Whatman No. 1 filter paper. The concentration of Pb in the supernatant was measured using a PinAAcle 900T Atomic Absorption Spectrophotometer (Perkin-Elmer) in Graphite-Furnace Atomic Absorption Spectrometry mode.

The adsorption capacity (q_e, mg/g) and the percentage removal (H, %) were calculated using Equations (1) and (2), respectively, as follow:

q_{e} = \frac{(C_{0} - C_{e}) . V}{m}

(1)

H = \frac{(C_{0} - C_{e}) .100}{C_{0}}

(2)

where C_o and C_e (mg/L) are the initial Pb²⁺ concentration and Pb²⁺ concentration at equilibrium, m (g) is the weight of the adsorbents, and V (L) is the volume of Pb²⁺ solution.

The adsorption equilibrium data for Pb²⁺ on bentonite and HNO₃-activated bentonite were analyzed using the linear forms of three two-parameter kinetic and isotherm models. These models were applied to the experimental values of q_e and C_e to derive the parameters of the equations. The linearized equations for the kinetic and isotherm models are presented in Table 1.

Table 1.

Adsorption kinetic and isotherm models.

	Functional form	Linear form	Plot	Parameters and constants
Kinetic
Pseudo-first order	$\frac{d q_{t}}{d t} = k_{1} (q_{e} - q_{t})$	ln $(q_{e} - q_{t}) = l n q_{e} - k_{1} t$	log (q_e − q_t) vs t	q_e: equilibrium adsorption capacity (mg/g); q_t: time adsorption capacity (mg/g); t: the contact time (min); k_1, k₂: First and second-order rate coefficient, respectively (g/mg.min); α: initial adsorption rate (mg/g.min); β: related to the extended of surface coverage and activation energy for chemisorption (g/mg).
Pseudo-second order	$\frac{d q_{t}}{d t} = k_{2} (q_{e} - q_{t})^{2}$	${\frac{t}{q}}_{t} = \frac{1}{k_{2} q_{e}^{2}} + \frac{1}{q_{e}} t$	log t/q_t vs t
Elovich equation	$\frac{d q_{t}}{d t} = α \exp (- β q_{t})$	$q_{t} = \frac{1}{β} \ln (α β) + \frac{1}{β} \ln t$	q_t vs lnt
Isotherm
Langmuir	$q_{e} = \frac{q_{m} K_{L} C_{e}}{1 + K_{L} C_{e}}$	$\frac{C_{e}}{q_{e}} = \frac{1}{K_{L} q_{m}} + \frac{C_{e}}{q_{m}}$	$\frac{C_{e}}{q_{e}}$ versus C_e	q_e: adsorption capacity (mg/g); q_m: the maximum adsorption capacity (mg/g); C_e: equilibrium concentration of the adsorbate (mg/L); K_L: the Langmuir adsorption constant (L/mg). K_F: the sorption affinity, (mg/kg)/(mg/L) ⁿ ; 1/n: the nonlinearity index (unitless); K_T: equilibrium association constant (l/mg); b: variation of the adsorption energy (J/mol) R: gas constant (8.314 J/mol.K); T: the absolute temperature in Kelvin (273 + °C).
Freundlich	q_e = K_F C_e^1/n	log q_e = log K_F + (1/n) log C_e	log q_e versus log C_e
Temkin	$q_{e} = \frac{R T}{b} \ln (K_{T} C_{e})$	$q_{e} = \frac{R T}{b} \ln K_{T} + \frac{R T}{b} \ln C_{e}$	q_e versus lnC_e

Design of experiments

Prior to the main experiments, screening tests were conducted to identify the most suitable ranges for each parameter. The screening experiments were designed to determine which factors had the most significant influence on Pb²⁺ adsorption. These results guided the selection of the conditions used in the Taguchi experimental design. The screening phase ensured that the experimental conditions covered both lower and upper limits, within which meaningful and measurable changes in Pb²⁺ adsorption could occur.

In this study, four key controllable factors were selected: the pH of the solution, Pb²⁺ concentration, adsorbent mass, and reaction time. Each factor was tested at five different levels to assess their impact on the removal efficiency of Pb²⁺ ions from aqueous solutions. The specific factors and their corresponding values are as follows:

pH (A): 3, 4, 5, 6, 7

Material mass (B): 0.01 g, 0.05 g, 0.1 g, 0.3 g, 0.5 g

Time (C): 10 min, 30 min, 60 min, 90 min, 120 min

Concentration (D): 10 mg/L, 30 mg/L, 50 mg/L, 80 mg/L, 100 mg/L

Utilizing the Taguchi experimental design, a total of 25 experiments were conducted, incorporating these four controllable parameters at five levels each. The experimental plan was organized in an L25 OA format, as presented in Table 2.

Table 2.

Orthogonal array of L25 for Pb²⁺ sorption by bentonite and HNO₃-activated bentonite.

Test	A	B	C	D
Test	pH	Material mass (g)	Concentration (mg/L)	Time (min)
1	3	0.01	10	10
2	3	0.05	30	30
3	3	0.1	50	60
4	3	0.3	80	90
5	3	0.5	100	120
6	4	0.01	30	60
7	4	0.05	50	90
8	4	0.1	80	120
9	4	0.3	100	10
10	4	0.5	10	30
11	5	0.01	50	120
12	5	0.05	80	10
13	5	0.1	100	30
14	5	0.3	10	60
15	5	0.5	30	90
16	6	0.01	80	30
17	6	0.05	100	60
18	6	0.1	10	90
19	6	0.3	30	120
20	6	0.5	50	10
21	7	0.01	100	90
22	7	0.05	10	120
23	7	0.1	30	10
24	7	0.3	50	30
25	7	0.5	80	60

To assess the relative importance of the factors influencing Pb²⁺ sorption, the study conducted a one-way ANOVA using Minitab 21 (Minitab LLC, Chicago, USA). The key outcomes of this analysis are the p-values and the corresponding percent contributions. Factors with the highest percent contributions have the most significant impact on the response.

Statistical methods

Taguchi method

The Taguchi method, created by Genichi Taguchi, is a popular approach for optimizing design variables because it effectively reduces both testing time and costs (Zolfaghari et al., 2011). This method employs a Taguchi crossed array layout, which includes an inner and an outer array. The inner array consists of an OA that covers all possible combinations of controllable factors. By leveraging this OA, the Taguchi method streamlines the process of identifying optimal experimental conditions (Taguchi, 1986). The calculation procedure for the Taguchi method involves several steps: calculating the signal-to-noise (S/N) ratios for each factor under investigation, determining delta values from these S/N ratios, and establishing the ranking of each factor (Yuce et al., 2022).

There are three common types of signal-to-noise (S/N) ratio analysis: (1) lower is better (LB), (2) nominal is best (NB), and (3) higher is better (HB). In this study, which aims to maximize lead Pb removal efficiency, the HB S/N ratio analysis is the most appropriate choice, as described in Equation (3).

\frac{S}{N} = - 10 \log_{10} (\frac{1}{n} \sum_{i = 1}^{n} \frac{1}{Y_{i}^{2}})

(3)

where n denotes the number of repetitions conducted under identical experimental conditions, and Y_i refers to the measurement results.

The delta values are determined by calculating the difference between the maximum and minimum S/N ratios for each parameter. These delta values are then ranked, with the largest delta indicating the most influential parameter. The rank is established based on this order, highlighting the parameters that have the greatest impact on the response variable.

Analysis of variance

Table 3 provides a comprehensive overview of essential concepts and formulas utilized in ANOVA. ANOVA is a statistical method employed to assess differences among group means and understand variability within datasets.

Table 3.

Key concepts and formulas in ANOVA analysis.

Concept	Description	Formula	Notes
Total sum of squares (SS_T)	Measures total variability in the dataset	$S S_{T} = \sum_{i = 1}^{N} (Y_{i} - \bar{Y})^{2}$	N refers to the total number of cases or observations in the orthogonal array used for the experimental design. Y_i is the experimental or numerical result for the i-th experiment. $\bar{Y}$ is the overall mean of all experimental results. P is one of the parameters being studied. j is the level number of the parameter P. t is the repetition of each level of the parameter P SY_j the sum of the experimental results involving this parameter P and level j
Sum of squares for error (SS_e)	Variability within groups, reflecting random error.	$S S_{e} = S S_{T} - S S_{A} - S S_{B} - S S_{C} - S S_{D} - S S_{E}$
Sum of squares for parameters (SS_p)	Variability attributed to different process parameters	$S S_{P} = \sum_{j = 1}^{t} \frac{{(S Y_{j})}^{2}}{t} - \frac{1}{N} {[\sum_{i = 1}^{N} Y_{i}]}^{2}$
Total degrees of freedom (df_total)	Total degrees of freedom in the analysis	df_total = N – 1
Degrees of freedom for parameter (df_P)	Degrees of freedom for each parameter being tested	df_P = k – 1
Variance for parameter (MS_P)	Variance associated with each tested parameter	MS_P = SS_P/df_P
Mean squared error (MS_e)	Average error variance	MS_e = SS_e/df_e
F-value	Ratio used to compare variances between groups.	F = MS_P/MS_e
Percentage contribution (ρ)	Contribution of each parameter to the total variability.	$ρ_{p} = \frac{S S_{P}}{S S_{T}}$

ANOVA: analysis of variance.

Results and discussion

Characterization of bentonite and HNO₃-activated bentonite

Textural properties

The textural properties, such as specific surface area, average pore size, and pore volume, were evaluated using established techniques, including the BET and Barrett–Joyner–Halenda models.

The N₂ adsorption–desorption isotherms for both bentonite and HNO₃-activated bentonite, illustrated in Figure 1, reveal a distinct hysteresis loop of H3 type (Amari et al., 2010). This specific type of hysteresis is indicative of the presence of slit-shaped or ink-bottle pores within the materials. The elevated N₂ adsorption/desorption curve for HNO₃-activated bentonite indicates that it possesses a higher nitrogen adsorption capacity, which can be attributed to an increased specific surface area. The findings underscore the effectiveness of the HNO₃-activation process in enhancing the bentonite's porosity and surface characteristics (Ibigbami et al., 2022).

Figure 1.

Nitrogen adsorption–desorption isotherms at 77 K of bentonite and HNO₃-activated bentonite.

Studies have shown that pore diameter reduction does not significantly affect the total pore volume (Fahmi et al., 2018; Gokce and Aktas, 2014). This observation is consistent with our study, where the pore diameter of HNO₃-activated bentonite (2.12 nm) is similar to that of raw bentonite (2.14 nm). However, the activation process likely leads to the formation of a greater number of smaller, more accessible pores. As a result, the overall pore volume increases from 0.06 cm³/g for raw bentonite to 0.07 cm³/g for HNO₃-activated bentonite (Table 4).

Table 4.

Specific surface area, pore volume, and pore diameter of bentonite and HNO₃-activated bentonite.

	Bentonite	HNO₃-activated bentonite
Specific surface area (m²/g)	62.98	70.79
Pore volume (cm³/g)	0.06	0.07
Pore diameter (nm)	2.14	2.12

Moreover, the specific surface area, which reflects the available surface for adsorption per gram, is higher for HNO₃-activated bentonite (70.79 m²/g) compared to raw bentonite (62.98 m²/g). This increase in surface area, combined with the enhanced pore volume, suggests that although individual pores may be slightly smaller, their increased number and expanded surface area provide more active sites for Pb²⁺ adsorption.

These findings align with previous research by Ibigbami et al. (2022), which reported an increase in specific surface area (79 m²/g) and pore volume (0.19 cm³/g) for bentonite after acid activation, compared to nonactivated bentonite (70 m²/g and 0.17 cm³/g) (Ibigbami et al., 2022). In another study, the specific surface area of raw bentonite was 69 m²/g, compared to 195 m²/g for acid-activated bentonite, highlighting significant enhancement. Pore volume also increased from 0.14 cm³/g to 0.47 cm³/g postactivation, indicating improved adsorbate accommodation (Amari et al., 2010). These changes underscore the superior adsorptive capabilities of acid-activated bentonite.

XRD spectra

XRD is a key technique for monitoring changes in the interlayer spacing of clay minerals. Figure 2 illustrates the XRD patterns of both bentonite and HNO₃-activated bentonite samples.

Figure 2.

XRD patterns of bentonite and HNO₃-activated bentonite.

The XRD analysis results presented in Figure 2 indicate that the bentonite material contains key mineral components, including montmorillonite (Al₂Si₄O₁₁(OH)₂), calcite (CaCO₃), and quartz (SiO₂), along with a smectite phase ((Na; K)AlSi₃O₈) (Amari et al., 2010; Krupskaya et al., 2019). Similar to bentonite, HNO₃-activated bentonite retains the mineral components of montmorillonite and quartz, but notably lacks calcite, suggesting that the acid treatment effectively dissolved or transformed this carbonate phase. Montmorillonite is the dominant mineral in both samples, exhibiting a strong peak intensity, indicative of its high purity and crystallinity.

In the case of bentonite, the montmorillonite peak appears at a 2θ angle of 15.146°, while in HNO₃-activated bentonite, this peak shifts slightly to 14.953°. This minor shift towards lower angles is significant, as it reflects an increase in interlayer spacing, which is a crucial indicator of the structural modifications occurring in the clay. These alterations suggest changes in the arrangement of both the tetrahedral and octahedral layers of the clay minerals (Ibigbami et al., 2022).

Furthermore, the activation process not only impacts the peak positions but also leads to variations in peak intensities. A reduction in intensity can indicate a loss of crystallinity or the emergence of amorphous phases, which may increase the reactivity and surface area of the HNO₃-activated bentonite. The observation of a notable peak at 3.209° following HNO₃ treatment reinforces the idea of mineral transformation, marking the transition from calcite to quartz dominance (Pawar et al., 2016). In brief, this XRD pattern highlights the mineralogical stability of montmorillonite and quartz while revealing significant structural changes resulting from HNO₃ treatment.

FT-IR analysis

Fourier transform infrared spectroscopy (FT-IR) captures the characteristic vibrations of bonds and functional groups within materials. In this study, we analyzed the functional groups of bentonite and HNO₃-activated bentonite using FT-IR (Perkin Elmer Spectrion 10.5.2, UK) with KBr as a base powder. The IR spectrum was recorded in the range of 4000 to 400 cm⁻¹ and are displayed in Figure 3.

Figure 3.

The infrared spectra of bentonite and HNO₃-activated bentonite.

The FTIR spectrum of bentonite reveals several key peaks that provide insights into its structure and composition. At 3437 cm⁻¹, a peak corresponds to the O-H stretching vibrations of silanol groups (Si–OH) and adsorbed water molecules, typical for clay minerals and indicating the presence of hydroxyl groups and adsorbed water (Bilgiç and Bilgiç, 2019; Olvianas et al., 2017; Zauska et al., 2021). The band at 1637 cm⁻¹ is attributed to H–O–H bending vibrations of adsorbed water, confirming bentonite's hygroscopic nature (Aroke et al., 2013; Venkatathri, 2006). The peak at 1030 cm⁻¹ is associated with Si–O stretching vibrations in the silicate framework, characteristic of alumino-silicates like bentonite (Zhu et al., 2023), while the 522 cm⁻¹ peak corresponds to Al–O–Si bending, indicating that octahedral sites are occupied by Al and tetrahedral sites by O–Si bonds (Harrou et al., 2024; Marouf et al., 2021). Additionally, the 468 cm⁻¹ peak is related to Si–O–Si bending, and the 405 cm⁻¹ peak is associated with Si–O–Mg (Taha et al., 2011). The 794 cm⁻¹ band suggests amorphous silica formation, while the 916 cm⁻¹ peak is attributed to Al–OH–Al (Toor et al., 2015). These characteristic absorptions are consistent with the presence of the dominant mineral phase, montmorillonite, a major component of bentonite.

However, after bentonite is activated by HNO₃, the FT-IR spectrum of HNO₃-activated bentonite shows notable changes. Acid activation causes proton (H⁺) penetration into the clay layers, attaching to -OH groups, leading to partial dissolution and dihydroxylation of the smectite structure (Ibigbami et al., 2022). The band between 3750 cm⁻¹ and 3100 cm⁻¹ corresponds to the O–H bond in the bentonite sheets (Aguilar et al., 2020; Bhattacharyya et al., 2014). Acid activation significantly reduces cations in the octahedral layer, causing the loss of water and hydroxyl groups within this range. The band at 3432 cm⁻¹ represents the -OH stretch, while the intensity of the OH stretch band at 3625 cm⁻¹ decreases due to acid attack. This peak is associated with OH bonds at the Al₂OH structural sites in aluminum-rich bentonite (Madejová, 2003). Other stretching bands at 3432 cm⁻¹, 1092 cm⁻¹, 1038 cm⁻¹, and 469 cm⁻¹ also decrease in intensity after activation (Komadel et al., 1990). The Si–O–Si stretch peaks at 512 cm⁻¹ and 469 cm⁻¹ remain after activation, and the 697 cm⁻¹ band corresponds to out-of-plane vibrations of Al–O and Si–O (Madejová, 2003).

The FTIR bands show minimal differences between bentonite and HNO₃-activated bentonite, indicating that acid activation affects both octahedral and tetrahedral sheets, as confirmed by XRD analysis. The slight shifts and intensity changes suggest modifications in cations and hydroxyl groups. However, the core structure remains largely intact, confirming that HNO₃ activation enhances adsorptive properties without significantly compromising the material's structural integrity (Sdiri et al., 2014).

Results of Taguchi and ANOVA method

Analysis of S/N ratio

To determine the removal efficiency of Pb²⁺ ions using bentonite and HNO₃-activated bentonite adsorbents, we selected the “larger is better” performance characteristics equation. The mean S/N ratio for each controllable parameter at any level was calculated by averaging the S/N ratios from four experiments conducted at that level. The experimental results for Pb²⁺ ion removal and the corresponding S/N ratio values are presented in Table 5. Additionally, Figure 4 illustrates the effects of four controllable factors on the averaged S/N ratios.

Figure 4.

Mean S/N ratio of bentonite (a) and HNO₃-activated bentonite (b).

Table 5.

The S/N ratio of each test.

Test	Factor				Adsorption capacity (q_e, mg/g)		S/N ratio
Test	pH	Mass	Concentration	Time	Bentonite	HNO₃-activated bentonite	Bentonite	HNO₃-activated bentonite
1	3	0.01	10	10	13.77	14.34	22.78	23.13
2	3	0.05	30	30	29.15	29.77	29.29	29.48
3	3	0.1	50	60	39.60	40.18	31.95	32.08
4	3	0.3	80	90	37.85	38.05	31.56	31.61
5	3	0.5	100	120	55.40	55.84	34.87	34.94
6	4	0.01	30	60	30.68	33.36	29.74	30.46
7	4	0.05	50	90	48.35	49.11	33.69	33.82
8	4	0.1	80	120	67.58	68.41	36.60	36.70
9	4	0.3	100	10	26.68	27.23	28.52	28.70
10	4	0.5	10	30	22.09	22.10	26.89	26.89
11	5	0.01	50	120	74.46	77.90	37.44	37.83
12	5	0.05	80	10	25.06	27.05	27.98	28.64
13	5	0.1	100	30	44.23	44.73	32.91	33.01
14	5	0.3	10	60	29.83	29.84	29.49	29.50
15	5	0.5	30	90	51.59	51.64	34.25	34.26
16	6	0.01	80	30	45.27	61.92	33.12	35.84
17	6	0.05	100	60	51.17	54.72	34.18	34.76
18	6	0.1	10	90	41.12	41.14	32.28	32.29
19	6	0.3	30	120	69.61	69.64	36.85	36.86
20	6	0.5	50	10	26.61	26.67	28.50	28.52
21	7	0.01	100	90	60.97	71.37	35.70	37.07
22	7	0.05	10	120	45.90	45.93	33.24	33.24
23	7	0.1	30	10	21.20	21.28	26.53	26.56
24	7	0.3	50	30	34.74	34.83	30.82	30.84
25	7	0.5	80	60	54.93	55.01	34.80	34.81

S/N ratio: signal to noise ratio.

The q_e values for Pb²⁺ represent the average removal efficiency of both bentonite and HNO₃-activated bentonite based on three replicates. The bold entries indicate the maximum signal-to-noise (S/N) ratio achieved among the 25 tests conducted.

The results in Table 5 demonstrated significant variations in adsorption capacity, with bentonite achieving Pb²⁺ adsorption levels ranging from 13.77 mg/g to 74.76 mg/g, and HNO₃-activated bentonite ranging from 14.34 mg/g to 77.90 mg/g. It is observed that HNO₃-activated bentonite consistently showed a higher adsorption capacity compared to bentonite. The differences in adsorption capacity (q_e) between HNO₃-activated bentonite and bentonite across the 25 tests varied obviously, with the largest differences observed in Test 16 (16.65 mg/g), Test 11 (3.44 mg/g), and Test 21 (10.40 mg/g). Most tests showed relatively slight differences in adsorption capacity, suggesting that while HNO₃ activation generally enhances performance, the extent of improvement varies based on experimental conditions. This enhanced performance can be attributed to the superior surface properties of HNO₃-activated bentonite, which benefits from an increased specific surface area and a greater volume of larger capillaries following activation, as indicated by previous BET results. These structural enhancements facilitate more effective interactions between the adsorbent and Pb²⁺ ions, allowing HNO₃-activated bentonite to retain more Pb²⁺ (Ibigbami et al., 2022).

The average S/N ratio results, shown in Table 6, indicate ranges of 22.78–37.44 for bentonite and 23.13–37.83 for HNO₃-activated bentonite. The S/N ratios for each controllable factor stage are derived from removal averages in Figure 4, illustrating the Taguchi method's logic. The strongest influence on the S/N ratio is from contact time, followed by initial Pb²⁺ concentration, pH, and the mass of the adsorbent materials. The maximum Pb²⁺ adsorption capacities are 74.76 mg/g (S/N = 37.44) for bentonite and 77.9 mg/g (S/N = 37.83) for HNO₃-activated bentonite. Under the same conditions (pH 5, 0.01 g mass, 50 mg/L concentration, 120 min), HNO₃-activated bentonite shows higher ion absorption due to increased active sites, porosity, and surface area following acid activation, confirming its superior Pb²⁺ removal capability.

Table 6.

Analysis of variance of bentonite and HNO₃-activated bentonite.

Model term		Sum of squares		Mean square		F-value		p-value		Delta value
Model term	DOF	Bentonite	HNO₃-activated bentonite	Bentonite	HNO₃-activated bentonite	Bentonite	HNO₃-activated bentonite	Bentonite	HNO₃-activated bentonite	Bentonite	HNO₃-activated bentonite
pH	4	26.90	34.60	6.73	8.65	5.66	6.19	.02	.01	2.9	3.41
Mass (g)	4	1.00	5.02	0.25	1.26	0.21	0.9	.93	.51	0.6	1.37
Conc. (mg/L)	4	59.85	73.62	14.96	18.40	12.59	13.17	.00	.00	4.3	4.69
Time (min)	4	223.64	215.98	55.91	53.99	47.05	38.65	.00	.00	8.94	8.8
Residual error	8	9.51	11.18	1.19	1.40

Figure 4 summarizes the mean S/N ratios for both bentonite and HNO₃-activated bentonite across four critical factors. Each factor is evaluated at five distinct levels, with the S/N ratios indicating the effectiveness of Pb²⁺ removal. For bentonite, contact time has the highest S/N ratio (35.8) at Level 5, showcasing its crucial role in enhancing adsorption, followed by concentration with a peak of 33.24, while pH and mass have lower influences, ranked third and fourth respectively. In HNO₃-activated bentonite, a similar trend is observed, with contact time again ranked highest (35.91), followed closely by concentration (33.7) and pH (33.65), while mass remains the least impactful (ranked fourth).

Implementation of ANOVA

ANOVA was conducted to assess the impact of each controllable parameter on the efficiency of Pb²⁺ ion removal, ensuring the reliability of the results and experimental conditions (Razmi and Ghasemi-Fasaei, 2018). The findings for both bentonite and HNO₃-activated bentonite are summarized in Table 6. The analysis indicates that pH has a significant effect on adsorption for both materials, with p-values of .02 for bentonite and 0.01 for HNO₃-activated bentonite; notably, HNO₃-activated bentonite shows greater sensitivity to pH variations, as reflected in its higher sum of squares. Conversely, the mass does not significantly affect adsorption for either material, as demonstrated by low sum of squares and high p-values (.93 for bentonite and .51 for HNO₃-activated bentonite). Both concentration and contact time exhibit highly significant effects, with very low p-values (0.00) and substantial variability, highlighting their importance in enhancing adsorption capacity.

Figure 5 shows the contribution percentages of factors affecting Pb²⁺ adsorption: contact time, initial concentration, pH, and material mass. Contact time is the most significant factor, accounting for 69.69% of variance in Pb²⁺ adsorption for bentonite and 63.45% for HNO₃-activated bentonite, highlighting its crucial role in enhancing adsorption capacity. In contrast, material mass has minimal impact, contributing only 0.31% for bentonite and 1.48% for HNO₃-activated bentonite. This aligns with the delta values in Table 6, where contact time has the highest delta (8.94 for bentonite, 8.8 for HNO₃-activated bentonite), confirming its influence. Previous studies also emphasized contact time and initial concentration as key parameters; for instance, a study using the Taguchi method found initial concentration to be the most influential factor, contributing 31% to Pb²⁺ removal and 43% to Hg²⁺ removal (Zolfaghari et al., 2011).

Figure 5.

Percentage contribution of parameters to Pb²⁺ adsorption capacity by bentonite and HNO₃-activated bentonite.

Regression analysis

Regression analysis is applied to model and analyze situations where different variables have a relationship between a dependent variable and one or more independent variables (Kıvak, 2014). In this study, regression analysis is used to calculate the equations predicting the theoretical adsorption amount and the actual adsorption amount of bentonite and HNO₃-activated bentonite. The prediction equations are constructed as linear models and quadratic regression models.

Table 7 presents prediction equations for Pb²⁺ adsorption using bentonite and HNO₃-activated bentonite, along with their R) values, which indicate how well each model explains the variability in adsorption results. The table includes linear equations that show simple relationships between Pb²⁺ adsorption and independent variables (denoted as a, b, c, and d, typically representing factors like pH, mass, concentration, and contact time). In contrast, quadratic regression curves incorporate squared and interaction terms, allowing for a more accurate representation of the relationships among the variables. The R² values reflect the percentage of variability in Pb²⁺ adsorption explained by the models; for bentonite, the linear model has an R² of 0.84, while the quadratic model significantly improves this to 0.96. For HNO₃-activated bentonite, the linear model has an R² of 0.80, and the quadratic model raises it to 0.95, highlighting the complexity of its adsorption behavior due to enhanced surface interactions. The findings indicate that quadratic regression models provide a better fit than linear models, emphasizing the importance of considering both individual effects and interactions among factors for effective optimization of the adsorption process in practical applications (Nas and Altan Özbek, 2020).

Table 7.

Prediction equations of bentonite and HNO₃-activated bentonite.

Bentonite		R²
Linear	0.78 + 2.45 a − 3.15 b + 0.1691 c + 0.3286 d	0.84
quadratic regression curve	142.7 – 56.1 a + 26.1 b − 2.66 c + 2.289 d + 4.74 a² – 51.8 b² + 0.00919 c² + 0.00895 d² + 5.30 a × b + .530 a × c – 0.394 a × d – 0.276 b × c – 0.087 b × d – 0.01654 c × d	0.96
HNO₃-activated bentonite		R²
Linear	−1.02 + 3.09 a − 11.50 b + 0.2102 c. + 0.3246 d	0.80
quadratic regression curve	157.0 – 62.5 a + 82.8 b − 3.20 c + 2.65 d + 5.23 a × a − 55.8 b × b + 0.00874 c × c + 0.01002 d × d − 8.4 a × b + 0.675 a × c − 0.456 a × d − 0.068 b × c − 0.091 b × d − 0.01964 c × d	0.95

Notes: a, b, c, and d represent pH, mass, concentration, and time, respectively.

The fit of the quadratic regression curve model for (a) bentonite and (b) HNO₃-activated bentonite is illustrated in Figure 6. This figure effectively demonstrates how well the quadratic model aligns with the experimental data, highlighting the strong predictive capabilities of the model for both materials. The close correspondence between the predicted values and the actual adsorption results underscores the quadratic model's ability to capture the complex interactions influencing adsorption behavior, providing a more accurate representation compared to simpler linear models.

Figure 6.

Quadratic regression curve model of bentonite (a) and HNO₃-activated bentonite (b).

Figure 7 presents contour plots for bentonite and HNO₃-activated bentonite, illustrating the intricate relationships between pH, mass, concentration, and contact time in Pb²⁺ adsorption. Plot (a) examines how varying pH levels and adsorbent dosages affect Pb²⁺ adsorption, indicating that certain pH ranges enhance adsorption efficiency. It shows that increasing the dosage improves removal efficiency, particularly at optimal pH levels. This underscores the importance of fine-tuning both pH and dosage to maximize Pb²⁺ removal, revealing that higher dosages at suitable pH create favorable conditions for stronger electrostatic interactions with Pb²⁺ ions (Mäki-Arvela and Murzin, 2013). Plot (b) highlights the relationship between pH levels and Pb²⁺ concentration, demonstrating that lower pH values significantly enhance adsorption efficiency, particularly at higher concentrations of Pb²⁺ ions. The plot likely shows distinct regions where adsorption increases, illustrating that maintaining a lower pH can optimize the removal of Pb²⁺ in solutions with high Pb²⁺ concentrations, ultimately improving the overall effectiveness of the adsorption process. Plot (c) demonstrates how varying pH levels affect removal efficiency over different contact times, indicating that longer durations at optimal pH significantly enhance Pb²⁺ adsorption. As contact time increases, the likelihood of Pb²⁺ ions interacting with the adsorbent surface also rises, promoting greater adsorption (Mariana et al., 2021). Optimal pH conditions favor electrostatic attractions and chemical interactions, leading to higher retention of Pb²⁺ ions (Radovic et al., 2001).

Figure 7.

Contour plot for bentonite and HNO₃-activated bentonite (a) pH and dosage, (b) pH and concentration, (c) pH and contact time, (d) dosage and concentration, (e) dosage and contact time, (f) concentration and contact time.

Plot (d) shows that increasing adsorbent dosage generally enhances Pb²⁺ adsorption rates, indicating a clear correlation between dosage and removal efficiency. However, this improvement continues only up to a certain concentration threshold, beyond which adsorption rates may plateau or decrease. This suggests that while additional adsorbent provides more active sites, excessive amounts may not significantly increase efficiency if Pb²⁺ ions are insufficient to occupy all sites (Obsa et al., 2024). Plot (e) examines the impact of varying adsorbent dosages on Pb²⁺ adsorption over time, indicating that while longer contact times generally enhance adsorption, higher dosages can lead to diminishing returns. As contact time increases, the likelihood of Pb²⁺ ions interacting with the adsorbent rises, but elevated dosages may saturate available active sites, limiting further adsorption (Huang et al., 2014). This saturation means that beyond a certain dosage, additional adsorbent does not significantly improve removal efficiency. Finally, plot (f) examines the interaction between Pb²⁺ concentration and contact time, suggesting that lower concentrations combined with extended contact times enhance adsorption efficiency. At these lower concentrations, more active sites on the adsorbent are available for binding, allowing for more effective interactions over time (Corda and Kini, 2020). Longer contact time facilitates the diffusion and adhesion of Pb²⁺ ions to the adsorbent surface, leading to improved removal rates (Mariana et al., 2021).

Adsorption kinetics and isotherms

To understand the mechanisms governing the adsorption of Pb²⁺ by bentonite and HNO₃-activated bentonite, the kinetic data were analyzed using three models: Pseudo second-order, Pseudo first-order, and Elovich. Additionally, the isotherm data were evaluated using the Langmuir, Freundlich, and Temkin models. The equations for these kinetic and isotherm models are presented in Table 1, while their respective linear parameters are detailed in Table 8.

Table 8.

Regression parameters of Pb²⁺ adsorption kinetics and Pb²⁺ adsorption isotherm.

	Kinetics
	Pseudo second-order					Pseudo first-order				Elovich
	q_e,exp	q_e,cal	k₂	χ²	R²	q_e,cal	k₁	χ²	R²	β	α	χ²	R²
	mg/g	mg/g	g/mg.min	χ²	R²	mg/g	g/mg.min	χ²	R²	g/mg	mg/g.min	χ²	R²
Bentonite	62.59	71.30	4.5×10⁻⁴	3.47	0.862	42.73	0.012	246.9	0.303	0.07	5.99	1.84	0.91
HNO₃-activated bentonite	63.54	71.81	4.9×10⁻⁴	2.84	0.874	42.08	0.013	5449	0.032	0.08	6.69	1.44	0.92
	Isotherms
	Langmuir				Freundlich				Temkin
	q_m	K_L	χ²	R²	1/n	K_F	χ²	R²	b	K_T	χ²	R²
	mg/g	L/mg	χ²	R²	1/n	(mg/kg)/(mg/L)ⁿ	χ²	R²	J/mol	1/mg	χ²	R²
Bentonite	48.22	4.46	3.11	0.65	0.11	37.64	0.28	0.939	603.1	11,049	0.15	0.97
HNO₃-activated bentonite	61.77	4.64	4.81	0.63	0.12	39.87	0.20	0.999	505.3	4000	0.07	0.99

The analysis of the linear error functions in Table 2 shows that the χ² values for the Elovich kinetic model were the smallest among the evaluated kinetic models, indicating a strong fit to the experimental data for Pb²⁺ ions. Additionally, the R² value for the Elovich model was the highest, suggesting it effectively represents the adsorption kinetics. The calculated equilibrium adsorption capacities (q_{e, cal}) from the Elovich model closely matched the experimental capacities (q_{e, exp}), further confirming that the Elovich model accurately captures the adsorption behavior of Pb²⁺ onto bentonite and HNO₃-activated bentonite and is relevant for describing the dynamics of the adsorption process.

The kinetic data analyzed using the linear form of the Elovich model indicate that Pb²⁺ adsorption occurs via chemisorption over an extended equilibrium time (Inyang et al., 2016). The initial sorption rate (α) for HNO₃-activated bentonite is measured at 6.69 mg/g.min, in contrast to 5.99 mg/g.min for regular bentonite. This higher α not only indicates that HNO₃-activated bentonite demonstrates a more rapid adsorption capacity but also suggests enhanced interaction between Pb²⁺ ions and the modified surface of the bentonite. Such improvements in adsorption kinetics can be attributed to the increased surface reactivity and porosity of HNO₃-activated bentonite, making it a more effective material for Pb²⁺-ion capture in aqueous solution (Inyang et al., 2016: 477; Tseng et al., 2022).

The analysis of the isotherm models in Table 9 indicates that Pb²⁺ adsorption by both bentonite and HNO₃-activated bentonite adheres to the Temkin model, evidenced by R² values exceeding 0.97 and χ² values below 0.15. This robust correlation suggests that the adsorption process is multilayered and significantly influenced by interactions between Pb²⁺ ions and the surfaces of both bentonites, a fundamental aspect of the Temkin model (Kalam et al., 2021). Notably, HNO₃-activated bentonite exhibits even higher R² values and lower χ² values than bentonite, indicating enhanced adsorption efficiency. This improvement is likely due to the chemical modifications introduced during the activation process, which may increase the availability of active sites or enhance the binding affinity for Pb²⁺ ions, thereby facilitating more effective adsorption (Ibigbami et al., 2022).

Table 9.

Effect of bentonite activation on the removal of various pollutants from water.

Pollutant	Adsorption capacity (mg/g)		Activation method	Efficiency improvement	Ref.
Pollutant	Bentonite	HNO₃-activated bentonite	Activation method	Efficiency improvement	Ref.
Toluene	66	197	H₂SO₄	3 times	(Amari et al., 2010)
Bezanyl Red	22.06	26.41	H₂SO₄	1.2 times	(Benguella and Yacouta-Nour, 2009)
Nylomine Green	14.78	10.64	H₂SO₄	1.39 times	(Benguella and Yacouta-Nour, 2009)
Cu²⁺	32.17	42.41	H₂SO₄	1.31 times	(Eren and Afsin, 2008)
Pb²⁺	18.9	25.8	Hexadecyl trimethyl ammonium bromide	1.36 times	(Dinh et al., 2022)
Pb²⁺	48.22	61.77	HNO₃	1.3 times	This study

Moreover, the Langmuir model analysis reveals that the maximum adsorption capacity (q_max) for HNO₃-activated bentonite is 61.77 mg/g, which surpasses the 48.22 mg/g capacity of bentonite. This difference not only highlights the superior adsorption capability of HNO₃-activated bentonite for Pb²⁺ ions but also suggests that the modifications during activation play a crucial role in optimizing its performance as an adsorbent.

Comparison with other studies

Acid activation of bentonite has been shown in several studies to significantly enhance its capacity to adsorb various pollutants. Research focused on H₂SO₄ activation for the adsorption of toluene, Cu²⁺, and Pb²⁺ (Amari et al., 2010; Dinh et al., 2022; Eren and Afsin, 2008) indicates that these treatments can markedly improve adsorption performance, as summarized in Table 9. In the current study, the adsorption capacity for Pb²⁺ using HNO₃-activated bentonite is reported at 61.77 mg/g, which is 1.3 times greater than the 48.22 mg/g capacity of natural bentonite. Although this capacity is lower than some findings in the literature (as shown in Table 9), the results demonstrate that HNO₃ activation effectively enhances Pb²⁺ adsorption. This underscores the viability of HNO₃ activation as a method for improving the adsorptive properties of bentonite, even if the improvements are less pronounced compared to other activation methods like H₂SO₄.

Conclusion

In this study, the activation of bentonite with HNO₃ significantly improved its properties, especially its surface area and structural characteristics, leading to enhanced Pb²⁺ adsorption capacity. Physical–chemical analyses (BET, XRD, and FTIR) confirmed that HNO₃ activation altered the bentonite's structure, increasing the availability of active sites for Pb²⁺ adsorption. Specifically, the BET surface area increased, while the XRD and FTIR spectra revealed modifications to the octahedral and tetrahedral sheets, enhancing interaction between the adsorbent and Pb²⁺ ions.

The Taguchi method with an L25 layout was employed to optimize the Pb²⁺ adsorption process, identifying contact time as the most significant factor for both raw and HNO₃-activated bentonite. The maximum Pb²⁺ adsorption capacity increased from 48.22 mg/g for raw bentonite to 61.77 mg/g for the activated material—1.3 times greater. The adsorption data fit the Temkin isotherm model, indicating a chemisorptive process, while the Elovich model described the kinetic data, suggesting a gradual adsorption. The increased sorption rate (α) for HNO₃-activated bentonite further supported this enhanced efficiency.

Future research should focus on optimizing the activation process further by varying acid concentrations and activation times to maximize adsorption capacity. Additionally, investigating the reusability and long-term stability of HNO₃-activated bentonite under real wastewater conditions could enhance its potential for large-scale water treatment. Thermodynamic studies and the use of nonlinearized models would provide deeper insights into the scaling process and adsorption mechanisms. Finally, examining the morphological changes postadsorption would help clarify the interaction of bentonite with Pb²⁺ and other ions.

Footnotes

Acknowledgment

The authors would like to thank the advanced solid waste treatment laboratory of Can Tho University funded by JICA for conducting experiments and analyzing the Pb²⁺.

Author contributions

The authors confirm the study conception and design: N. X. Loc and D. T. M. Phuong; data collection: N. T. A. Thu; analysis and interpretation of results: N. T. A. Thu; draft manuscript preparation: N. X. Loc and D. T. M. Phuong. The results were evaluated by all authors, and the final version of the manuscript was approved.

Declaration of conflicting interests

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

Funding

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

ORCID iD

Do Thi My Phuong

References

Abdulraheem

Al-Khafaji

Hashim

, et al. (2020) Natural filtration unit for removal of heavy metals from water. In: IOP Conference Series: Materials Science and Engineering, p. 012034. IOP Publishing.

Aguilar

Almeida-Naranjo

Aldás

, et al. (2020) Acid activation of bentonite clay for recycled automotive oil purification. In: E3S Web of Conferences, p. 03002. EDP Sciences.

Al-Essa

(2018) Activation of Jordanian bentonite by hydrochloric acid and its potential for olive mill wastewater enhanced treatment. Journal of Chemistry 2018: 8385692.

Alexander

Ahmad Zaini

Surajudeen

, et al. (2019) Surface modification of low-cost bentonite adsorbents—a review. Particulate Science and Technology 37: 538–549.

Amari

Chlendi

Gannouni

, et al. (2010) Optimised activation of bentonite for toluene adsorption. Applied Clay Science 47: 457–461.

Anna

Kleopas

Constantine

, et al. (2015) Adsorption of Cd (II), Cu (II), Ni (II) and Pb (II) onto natural bentonite: Study in mono-and multi-metal systems. Environmental Earth Sciences 73: 5435–5444.

Ardelia

Saviola

Wijaya

, et al. (2024) Sulfuric acid-activated Indonesian natural bentonite as solid acid catalysts in microwave-protocol nitrobenzene synthesis. Iranian Journal of Catalysis 14: 1–9.

Aroke

Abdulkarim

Ogubunka

(2013) Fourier-transform infrared characterization of kaolin, granite, bentonite and barite. ATBU journal of Environmental Technology 6: 42–53.

Aziz

KHH

Mustafa

Omer

, et al. (2023) Heavy metal pollution in the aquatic environment: Efficient and low-cost removal approaches to eliminate their toxicity: A review. RSC Advances 13: 17595–17610.

10.

Bayram

Ustunisik

Önal

, et al. (2021) Optimization of bleaching power by sulfuric acid activation of bentonite. Clay Minerals 56: 148–155.

11.

Benguella

Yacouta-Nour

(2009) Adsorption of bezanyl red and nylomine green from aqueous solutions by natural and acid-activated bentonite. Desalination 235: 276–292.

12.

Bhattacharyya

SenGupta

Sarma

(2014) Interactions of the dye, rhodamine B with kaolinite and montmorillonite in water. Applied Clay Science 99: 7–17.

13.

Bilgiç

(2019) Application of Fourier transform infrared (FTIR) spectroscopy to analysis of clays. Nevşehir Journal of Science and Technology 8: 37–46.

14.

Birniwa

Habibu

Abdullahi

, et al. (2023) Membrane technologies for heavy metals removal from water and wastewater: A mini review. Case Studies in Chemical and Environmental Engineering 9: 100538.

15.

Borah

Nath

Saikia

(2022) Modification of bentonite clay & its applications: A review. Reviews in Inorganic Chemistry 42: 265–282.

16.

Bouida

Rafatullah

Kerrouche

, et al. (2022) A review on cadmium and lead contamination: Sources, fate, mechanism, health effects and remediation methods. Water 14: 3432.

17.

Carrado

Komadel

(2009) Acid activation of bentonites and polymer-clay nanocomposites. Elements 5: 111–116.

18.

Chen

Y-G

W-M

Yang

X-M

, et al. (2011) Effect of contact time, pH, and ionic strength on cd (II) adsorption from aqueous solution onto bentonite from Gaomiaozi, China. Environmental Earth Sciences 64: 329–336.

19.

Collin

Venkatraman

Vijayakumar

, et al. (2022) Bioaccumulation of lead (Pb) and its effects on human: A review. Journal of Hazardous Materials Advances 7: 100094.

20.

Corda

Kini

(2020) Recent studies in adsorption of Pb (II), Zn (II) and Co (II) using conventional and modified materials: A review. Separation Science and Technology 55: 2679–2698.

21.

Dabrowski

Hubicki

Podkościelny

, et al. (2004) Selective removal of the heavy metal ions from waters and industrial wastewaters by ion-exchange method. Chemosphere 56: 91–106.

22.

Dinh

V-P

Nguyen

P-T

Tran

M-C

, et al. (2022) HTDMA-modified bentonite clay for effective removal of Pb (II) from aqueous solution. Chemosphere 286: 131766.

23.

Eren

Afsin

(2008) An investigation of Cu (II) adsorption by raw and acid-activated bentonite: A combined potentiometric, thermodynamic, XRD, IR, DTA study. Journal of Hazardous Materials 151: 682–691.

24.

Ewis

Benamor

Ba-Abbad

, et al. (2020) Removal of oil content from oil-water emulsions using iron oxide/bentonite nano adsorbents. Journal of Water Process Engineering 38: 101583.

25.

Fahmi

Jol

Singh

(2018) Physical modification of biochar to expose the inner pores and their functional groups to enhance lead adsorption. RSC advances 8: 38270–38280.

26.

Wang

(2011) Removal of heavy metal ions from wastewaters: A review. Journal of Environmental Management 92: 407–418.

27.

Gokce

Aktas

(2014) Nitric acid modification of activated carbon produced from waste tea and adsorption of methylene blue and phenol. Applied Surface Science 313: 352–359.

28.

Googerdchian

Moheb

Emadi

, et al. (2018) Optimization of Pb (II) ions adsorption on nanohydroxyapatite adsorbents by applying Taguchi method. Journal of Hazardous Materials 349: 186–194.

29.

Kang

Wang

, et al. (2019) Clay mineral adsorbents for heavy metal removal from wastewater: A review. Environmental Chemistry Letters 17: 629–654.

30.

Harrou

Lechheb

El Ouahabi

, et al. (2024) Physico-chemical properties and microstructure of bentonite in highly alkaline environments. Clays and Clay Minerals 72: e15.

31.

Huang

Chen

, et al. (2014) Adsorption of Pb (II) on mesoporous activated carbons fabricated from water hyacinth using H₃PO₄ activation: Adsorption capacity, kinetic and isotherm studies. Applied Surface Science 293: 160–168.

32.

Ibigbami

Adeola

Olawade

, et al. (2022) Pristine and activated bentonite for toxic metal removal from wastewater. Water Practice & Technology 17: 784–797.

33.

Inyang

Onwawoma

Bae

(2016) The Elovich equation as a predictor of lead and cadmium sorption rates on contaminant barrier minerals. Soil and Tillage Research 155: 124–132.

34.

Jalees

(2020) Synthesis and application of magnetized nanoparticles to remove lead from drinking water: Taguchi design of experiment. Journal of Water, Sanitation and Hygiene for Development 10: 56–65.

35.

Jha

Kumar

Jeong

, et al. (2012) Review on solvent extraction of cadmium from various solutions. hydrometallurgy 111: 1–9.

36.

Kalam

Abu-Khamsin

Kamal

, et al. (2021) Surfactant adsorption isotherms: A review. ACS omega 6: 32342–32348.

37.

Kinoti

Karanja

Nthiga

, et al. (2022) Review of clay-based nanocomposites as adsorbents for the removal of heavy metals. Journal of Chemistry 2022: 7504626.

38.

Kıvak

(2014) Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts. Measurement 50: 19–28.

39.

Komadel

Schmidt

Madejová

, et al. (1990) Alteration of smectites by treatments with hydrochloric acid and sodium carbonate solutions. Applied Clay Science 5: 113–122.

40.

Krupskaya

Zakusin

Tyupina

, et al. (2019) Transformation of structure and adsorption properties of montmorillonite under thermochemical treatment. Geochemistry International 57: 314–330.

41.

Kul

Koyuncu

(2010) Adsorption of Pb (II) ions from aqueous solution by native and activated bentonite: Kinetic, equilibrium and thermodynamic study. Journal of Hazardous Materials 179: 332–339.

42.

Kurniawan

Chan

W-H

, et al. (2006) Physico–chemical treatment techniques for wastewater laden with heavy metals. Chemical Engineering Journal 118: 83–98.

43.

Litu

Buema

Mosoarca

, et al. (2024) Copper ion removal by adsorption using fly ash-based geopolymers: Process optimization insights from Taguchi and ANOVA statistical methods. Materials 17: 3992.

44.

Liu

(2007) Polymer modified clay minerals: A review. Applied Clay Science 38: 64–76.

45.

Madan

Wasewar

(2017) Optimization for benzeneacetic acid removal from aqueous solution using CaO₂ nanoparticles based on Taguchi method. Journal of Applied Research and Technology 15: 332–339.

46.

Madejová

(2003) FTIR techniques in clay mineral studies. Vibrational Spectroscopy 31: 1–10.

47.

Mäki-Arvela

Murzin

(2013) Effect of catalyst synthesis parameters on the metal particle size. Applied Catalysis A: General 451: 251–281.

48.

Mariana

Mulana

Juniar

, et al. (2021) Development of biosorbent derived from the endocarp waste of Gayo coffee for lead removal in liquid wastewater—effects of chemical activators. Sustainability 13: 3050.

49.

Marouf

Dali

Boudouara

, et al. (2021) Study of adsorption properties of bentonite clay. In: Montmorillonite Clay. United Kingdom: IntechOpen. DOI: 10.5772/intechopen.75461

50.

Mustapha

Zhang

, et al. (2021) Taguchi and ANOVA analysis for the optimization of the microencapsulation of a volatile phase change material. Journal of Materials Research and Technology 11: 667–680.

51.

Nas

Altan Özbek

(2020) Optimization of the machining parameters in turning of hardened hot work tool steel using cryogenically treated tools. Surface Review and Letters 27: 1950177.

52.

Noyan

Önal

Sarıkaya

(2007) The effect of sulphuric acid activation on the crystallinity, surface area, porosity, surface acidity, and bleaching power of a bentonite. Food Chemistry 105: 156–163.

53.

Obsa

Shibeshi

Mulugeta

, et al. (2024) A green composite of sodium carboxymethyl cellulose and amino-decorated cellulose reinforced with modified bentonite for removal of lead (II): Kinetics and isotherm studies. Materials Today Communications: 41: 110412.

54.

Olvianas

Widiyatmoko

Petrus

HTBM

(2017) IR spectral similarity studies of geothermal silica-bentonite based geopolymer. In: AIP Conference Proceedings, AIP Publishing.

55.

Panayotova

Velikov

(2003) Influence of zeolite transformation in a homoionic form on the removal of some heavy metal ions from wastewater. Journal of Environmental Science and Health, Part A 38: 545–554.

56.

Park

Wang

Han

, et al. (2019) Evaluation of the colloidal stability and adsorption performance of reduced graphene oxide–elemental silver/magnetite nanohybrids for selected toxic heavy metals in aqueous solutions. Applied Surface Science 471: 8–17.

57.

Pawar

Bajaj

Lee

S-M

(2016) Activated bentonite as a low-cost adsorbent for the removal of Cu (II) and Pb (II) from aqueous solutions: Batch and column studies. Journal of Industrial and Engineering Chemistry 34: 213–223.

58.

Pei

Zheng

, et al. (2021) The use of bentonite and organic amendments for remediation of Cd contaminated fields: An environmental perspective. Land Degradation & Development 32: 3639–3652.

59.

Prabhu

(2018) A review on removal of heavy metal ions from waste water using natural/modified bentonite. In: MATEC Web of Conferences, EDP Sciences, 02021.

60.

Radovic

Moreno-Castilla

Rivera-Utrilla

(2001) Carbon materials as adsorbents in aqueous solutions. Chemistry and Physics of Carbon 27: 227–406.

61.

Rashid

Shafiq

Akhter

, et al. (2021) A state-of-the-art review on wastewater treatment techniques: The effectiveness of adsorption method. Environmental Science and Pollution Research 28: 9050–9066.

62.

Razmi

Ghasemi-Fasaei

(2018) Investigation of Taguchi optimization, equilibrium isotherms, and kinetic modeling for phosphorus adsorption onto natural zeolite of clinoptilolite type. Adsorption Science & Technology 36: 1470–1483.

63.

Rezapour

Abdollahi

Khorrami

, et al. (2016) Application of raw, HCl-and H2SO4-activated bentonite as adsorbents for the removal of Zn2+ and Pb2+ from aqueous solution. Desalination and Water Treatment 57: 3654–3663.

64.

Sarran

AbdulRazak

Abid

, et al. (2024) Oily wastewater treatment by using Fe₃O₄/bentonite in fixed-bed adsorption column. ChemEngineering 8: 92.

65.

Sdiri

Higashi

Jamoussi

(2014) Adsorption of copper and zinc onto natural clay in single and binary systems. International Journal of Environmental Science and Technology 11: 1081–1092.

66.

Singh

Kumar

(2019) Role of Taguchi and grey relational method in optimization of machining parameters of different materials: A review. Acta Electronica Malaysia (AEM) 3: 19–22.

67.

Taguchi

(1986) Introduction to quality engineering: Designing quality into products and processes .

68.

Taha

Suleiman

Musa

(2011) Performance of Sudanese activated bentonite in bleaching cottonseed oil. J Bangladesh Chem Soc 24: 191–201.

69.

Tang

Zheng

Teng

, et al. (2016) Chemical coagulation process for the removal of heavy metals from water: A review. Desalination and Water Treatment 57: 1733–1748.

70.

Toor

Jin

(2012) Adsorption characteristics, isotherm, kinetics, and diffusion of modified natural bentonite for removing diazo dye. Chemical Engineering Journal 187: 79–88.

71.

Toor

Jin

Dai

, et al. (2015) Activating natural bentonite as a cost-effective adsorbent for removal of Congo-red in wastewater. Journal of Industrial and Engineering Chemistry 21: 653–661.

72.

Tseng

R-L

Tran

Juang

R-S

(2022) Revisiting temperature effect on the kinetics of liquid–phase adsorption by the Elovich equation: A simple tool for checking data reliability. Journal of the Taiwan Institute of Chemical Engineers 136: 104403.

73.

Türkmen

Gül

Çelik

(2008) A Taguchi approach for investigation of some physical properties of concrete produced from mineral admixtures. Building and Environment 43: 1127–1137.

74.

Ullah

Hussain

Gul

, et al. (2016) Use of HCl-modified bentonite clay for the adsorption of Acid Blue 129 from aqueous solutions. Desalination and Water Treatment 57: 8894–8903.

75.

Venkatathri

(2006) Characterization and catalytic properties of a naturally occurring clay, bentonite. Bulletin of Catalysis Society of India 5: 61–72.

76.

Vimonses

Lei

Jin

, et al. (2009) Kinetic study and equilibrium isotherm analysis of Congo red adsorption by clay materials. Chemical Engineering Journal 148: 354–364.

77.

Wang

, et al. (2016) Comparison study of phosphorus adsorption on different waste solids: Fly ash, red mud and ferric–alum water treatment residues. Journal of Environmental Sciences 50: 79–86.

78.

Yuce

Nielsen

Wargocki

(2022) The use of Taguchi, ANOVA, and GRA methods to optimize CFD analyses of ventilation performance in buildings. Building and Environment 225: 109587.

79.

Zauska

Bova

Benova

, et al. (2021) Thermosensitive drug delivery system SBA-15-PEI for controlled release of nonsteroidal anti-inflammatory drug diclofenac sodium salt: A comparative study. Materials 14: 1880.

80.

Zhang

, et al. (2015) Kinetic and isotherms studies of phosphorus adsorption onto natural riparian wetland sediments: Linear and non-linear methods. Environmental Monitoring and Assessment 187: 1–11.

81.

Zhu

Chen

Zhou

, et al. (2016) Adsorbents based on montmorillonite for contaminant removal from water: A review. Applied Clay Science 123: 239–258.

82.

Zhu

Fan

Zhou

, et al. (2019) Removal of chelated heavy metals from aqueous solution: A review of current methods and mechanisms. Science of The Total Environment 678: 253–266.

83.

Zhu

Kou

Zhou

, et al. (2023) Preparation of amphiphilic chitosan-loaded bentonite adsorbent and its performance in removing organic matter from coking wastewater. Polymers 15: 1588.

84.

Zolfaghari

Esmaili-Sari

Anbia

, et al. (2011) Taguchi optimization approach for Pb (II) and Hg (II) removal from aqueous solutions using modified mesoporous carbon. Journal of Hazardous Materials 192: 1046–1055.

Optimization and modeling of Pb 2+ ions adsorption on bentonite and HNO 3 -activated bentonite using Taguchi and ANOVA methods

Abstract

Keywords

Highlights

Introduction

Materials and methods

Materials and chemicals

Preparation of HNO3-activated bentonite

Characterization methods

Batch adsorption experiments

Design of experiments

Statistical methods

Taguchi method

Analysis of variance

Results and discussion

Characterization of bentonite and HNO3-activated bentonite

Textural properties

XRD spectra

FT-IR analysis

Results of Taguchi and ANOVA method

Analysis of S/N ratio

Implementation of ANOVA

Regression analysis

Adsorption kinetics and isotherms

Comparison with other studies

Conclusion

Footnotes

Acknowledgment

Author contributions

Declaration of conflicting interests

Funding

ORCID iD

References

Preparation of HNO₃-activated bentonite

Characterization of bentonite and HNO₃-activated bentonite