Adsorption of the Drug Chlorhexidine Using Organoclay

Abstract

Organoclays have been used in drug adsorption processes due being cheap and environmentally friendly materials with a good cost benefit for the water treatment industry. The present work evaluated the adsorption of chlorhexidine, an antimicrobial agent, on the organophilic clay Cloisite 30B by using a 2³ fractional factorial design. The main and interactive factors studied were the initial chlorhexidine concentration (0.4 and 0.6 mmol/L), adsorbent mass (0.3 and 0.5 g), and contact time (1 and 6 h). The organophilic clay Cloisite 30B was characterized by XRD and FTIR. To evaluate the impact of pH on the adsorption process, a range from 1 to 13 was used, in increments of one pH unit. The chlorhexidine adsorption parameters used the following adsorption conditions: an initial pH of 6, 200 rpm and a reaction temperature of 25 °C. Kinetic data followed the pseudo-second order model, while equilibrium data fit best to the Sips isotherm, suggesting high affinity and capacity. The maximum removal efficiency reached 95.77%, mainly influenced by the initial chlorhexidine concentration. These findings demonstrate the potential of organoclay for removing pharmaceutical contaminants from water pre-treatment of industrial effluents.

1. Introduction

The intensification of industrial processes, coupled with accelerated population growth, has markedly increased the anthropogenic load of potentially toxic substances into aquatic ecosystems, jeopardizing water security and ecosystem services. This scenario is particularly concerning given the escalating water demand of the strategic sector, including the agribusiness, pharmaceutical, and food processing industries, which contribute directly or indirectly to the discharge of recalcitrant micropollutants into the environment. Among these, pharmaceuticals stand out due to their high usage rates, persistence, and bioactivity even at trace concentrations [1].

Pharmaceuticals are increasingly categorized as emerging contaminants (ECs) owing to their lipophilic character, low biodegradability, and potential for bioaccumulation and pseudo-persistence in environmental matrices. Environmental monitoring studies have reported concentrations of active pharmaceutical ingredients (APIs) ranging from ng/L to low µg/L in surface waters across urban and peri-urban regions [2]. These compounds typically enter aquatic environments via incomplete human or veterinary metabolism, hospital and domestic effluents, and the improper disposal of expired medications. Given that conventional wastewater treatment plants (WWTPs) were not engineered to address such contaminants, removal efficiencies for APIs such as chlorhexidine are often below 20–30% [3], amplifying ecological risks and complicating reuse strategies.

To mitigate these impacts, several advanced treatment technologies have been investigated, including advanced oxidation processes (AOPs), membrane separation, biological degradation, and chemical precipitation. While each approach presents distinct advantages, they are often constrained by high operational costs, complex infrastructure, high energy demands, or the generation of hazardous byproducts. In contrast, adsorption technologies have emerged as a cost-effective, scalable, and technically simple alternative, particularly suitable for decentralized or modular applications in resource-constrained settings [4,5,6].

Adsorption is governed by physical and/or chemical interactions between the adsorbate and the surface or pores of a solid-phase adsorbent. The efficacy of this process is influenced by multiple parameters, including the initial contaminant concentration, solution pH, adsorbent dosage, and temperature, as well as the surface area, porosity, and surface chemistry of the adsorbent [7]. Within this context, there is growing scientific and technological interest in the development of low-cost, high-performance adsorbents, particularly those derived from naturally abundant or waste-derived precursors, in alignment with circular economy principles [8].

Clays and their modified derivatives, including organo-functionalized montmorillonites, have attracted considerable attention as versatile adsorbent platforms due to their high cation exchange capacity, surface reactivity, and structural tunability. Among these, Cloisite 30B, a quaternary ammonium-modified montmorillonite, has shown promise in diverse sorption applications. One key advantage of organoclays over carbon-based materials is their lower cost.

However, despite its recognized organophilic character and intercalation capacity, its application for the removal of cationic pharmaceutical species such as chlorhexidine remains underexplored in the literature, particularly in terms of nonlinear kinetic and isotherm modeling under environmentally relevant conditions [9,10,11].

Therefore, this study aims to evaluate the performance of Cloisite 30B as a sustainable and efficient adsorbent for the removal of chlorhexidine in aqueous media. Its specific objectives include the following: (i) structural characterization of the organoclay before and after adsorption; (ii) investigation of the influence of pH and operational parameters on adsorption performance; and (iii) kinetic and equilibrium modeling using nonlinear approaches to elucidate the adsorption mechanism. By addressing these objectives, the work contributes to the strategic valorization of modified clays for pharmaceutical pollutant removal, supporting the development of advanced materials for wastewater treatment in line with environmental, economic, and regulatory imperatives [12,13].

This study explores the application of organoclay as a cost-effective and environmentally sustainable adsorbent for the removal of chlorhexidine from aqueous media under batch conditions. The investigation encompasses a comprehensive assessment of pH’s influence on adsorption efficiency, as well as detailed kinetic and equilibrium analyses. Adsorption kinetics were evaluated using nonlinear formulations of the pseudo-first order (PFO) and pseudo-second order (PSO) models, while equilibrium data were fitted to a suite of nonlinear isotherm models, including Langmuir, Freundlich, Sips, Temkin, and Redlich–Peterson. The findings underscore the viability of repurposing organoclay as a functional material for advanced wastewater treatment, with promising implications for the mitigation of pharmaceutical contaminants in aquatic environments.

2. Materials and Methods

2.1. Materials

The Cloisite 30B nanoclay, employed as the adsorbent in the experimental protocols, was procured from Southern Clay Products, headquartered in Gonzales, TX, USA. The active pharmaceutical compound utilized in the preparation of the synthetic effluent was chlorhexidine, sourced from a commercial supplier and applied in its as-received form, without undergoing further treatment or purification.

Table 1. Chlorhexidine drug properties.

Characteristics	Properties
Name	Chlorhexidine
CAS no.	55-56-1
Molecular formula	C22H30Cl2N10
Chemical structure
Molecular mass (g/mol)	505.45
Melting point (°C)	134
Solubility in water (g/L)	0.8
Absorbance (nm)	260

presents the main physicochemical properties of the chlorhexidine (Sigma-Aldrich, St. Louis, MO, USA), used in this study.

2.2. Characterizations

X-ray Diffraction (XRD): The synthesized materials were characterized using a SHIMADZU XRD-6000 diffractometer (Kyoto, Japan), operating with a Cu Kα radiation source (λ = 1.5406 Å), under an accelerating voltage of 40 kV and a current of 30 mA. Diffraction patterns were acquired over a 2θ range of 3° to 50°, with a scanning rate of 2°/min and a step size of 0.02°, employing a continuous goniometric sweep for high-resolution phase identification.

Fourier Transform Infrared Spectroscopy (FTIR): FTIR analyses were conducted using a VERTEX 70 spectrophotometer (Bruker, Bremen, Germany), operating within the spectral range of 4000 to 500 cm⁻¹. Sample pellets were prepared by homogenizing 0.0070 g of clay with 0.10 g of spectroscopic-grade KBr, followed by compression under a load of 5 tons for 30 s to form translucent disks suitable for infrared transmission measurements.

2.3. Influence of pH

To evaluate the impact of pH on the adsorption process, a series of flasks were prepared containing 0.5 g of organoclay and 50 mL of chlorhexidine solution at a concentration of 0.06 mmol/L. The pH of each solution was individually adjusted across a range from 1 to 13, in increments of one pH unit. The suspensions were subjected to agitation on a shaking table at 200 rpm for 1 h at a controlled temperature of 25 °C. Following this period, the mixtures were filtered, and the resulting filtrates were analyzed via UV-Vis spectrophotometry to determine the amount of chlorhexidine adsorbed per mmol of clay [14].

2.4. Adsorption Tests

The adsorption assays employed chlorhexidine as the target compound, so fixed-volume solutions (50 mL) were prepared within 125 mL Erlenmeyer flasks containing the organoclay Cloisite 30B as the adsorbent phase. The experimental conditions were defined according to a 2³ factorial design, varying the following three independent variables: adsorbent mass (0.3 and 0.5 g), initial chlorhexidine concentration (0.4 and 0.6 mmol/L), and contact time (1 and 6 h). All tests were performed under constant agitation at 200 rpm using a shaking table, and maintained at a controlled temperature of 25 °C.

Post-Adsorption Analysis: Upon completion of the predetermined contact time, the samples were filtered to separate the solid phase, and the residual concentration of chlorhexidine in the supernatant was quantified via UV-Vis spectrophotometry. Based on the obtained absorbance data, the adsorption capacity (q_eq) and removal efficiency (%Remo) were calculated using the following equations [15]:

$$\%\mathrm{R}\mathrm{e}\mathrm{m}\mathrm{o}=\left({\displaystyle \frac{C_0-C_f}{C_0}}\right)·100$$

(1)

$$q_{eq}={\displaystyle \frac{V}{m}}·(C_0-C_f)$$

(2)

where C₀ (mmol/L) is the initial concentration of the drug, C_f (mmol/L) is the final concentration after the contact time, V (L) is the volume of the solution, and m (g) is the mass of the adsorbent.

The evaluation of the kinetic models was performed using statistical goodness-of-fit metrics, including the coefficient of determination (R²) and the adjusted R², to ensure robust interpretation of model accuracy and predictive capability [16,17,18,19].

2.5. Kinetic Study

Kinetic adsorption experiments were conducted using 150 mL of chlorhexidine solution at a concentration of 0.05 mmol/L and 1.5 g of organoclay. The suspensions were agitated on a shaking table at 200 rpm and maintained at a constant temperature of 25 °C. The pH was not adjusted, remaining at the native value of the solution, as prior investigations demonstrated minimal influence of this parameter on the adsorption kinetics.

Aliquots were withdrawn at predetermined time intervals ranging from 0 to 30 min. All measurements were performed in duplicate, and the residual chlorhexidine concentration (C_f) was quantified via UV-Vis spectrophotometry.

2.6. Adsorption Isotherms

To construct the adsorption isotherm, chlorhexidine solutions with initial concentrations ranging from 0.004 to 2.0 mmol/L were prepared. For each experimental run, 150 mL of the respective solution was combined with 1.5 g of organoclay and subjected to agitation on a shaking table at 200 rpm for 60 min at a controlled temperature of 25 °C. The pH of the suspensions was maintained at a constant value, in accordance with prior findings indicating negligible impact on adsorption performance. Following the equilibration period, the mixtures were filtered, and the residual chlorhexidine concentrations were quantified via UV-Vis spectrophotometry.

3. Results

3.1. Characterization

3.1.1. X-Ray Diffraction

Figure 1 shows the diffractograms of organoclay before and after the chlorhexidine adsorption process.

In the untreated organoclay, a prominent diffraction peak corresponding to the basal plane (001) is observed at a 2θ angle, indicative of interlayer spacing (d₀₀₁) of 17.73 Å (1.773 nm), consistent with values reported in the literature for smectite-type clays [12,20,21,22]. In addition to the basal reflection, secondary diffraction peaks are also present, which are attributed to mineral impurities of non-smectite origin, such as quartz [23,24].

The selection of Cloisite 30B as the organoclay adsorbent is strategically justified by its expanded basal spacing relative to natural clays, which enhances its affinity and interaction with organic molecules. This structural feature is evidenced in the diffractogram by a shift toward lower diffraction angles, signifying an increased interlayer distance that facilitates molecular intercalation.

Following chlorhexidine adsorption, the basal peak exhibited a shift to 1.855 nm, indicating a modest expansion of the clay lamellae. Nevertheless, the preservation of the primary diffraction reflections and the lack of significant changes in peak intensities demonstrate that the crystalline structure of Cloisite 30B remained intact, thereby maintaining its chemical stability. These findings imply that the adsorption process predominantly occurred at the surface or the outer regions of the lamellae, with minimal intercalation into the clay interlayers.

The orientations of organic chains within clay minerals were first proposed by the authors of [25]. Depending on the layer charge (i.e., the interlayer cation density or the packing density of alkylammonium ions) and the chain length of the organic cation, various molecular arrangements can form within the interlayer spaces. Additionally, surface geometry and the degree of ion exchange may also influence these configurations.

3.1.2. Fourier Transform Infrared Spectroscopy

Figure 2 shows the infrared spectrum of organoclay.

A distinct absorption band appears at approximately 3626 cm⁻¹, corresponding to the stretching vibrations of the structural hydroxyl groups inherent to the clay crystal lattice [26,27,28]. Additionally, the broad band centered around 3400 cm⁻¹ is associated with the stretching vibrations of hydroxyl groups from physically adsorbed water molecules within the smectite interlayers [26,29].

The absorption bands observed at 2920 cm⁻¹ and 2849 cm⁻¹ are attributed, respectively, to the asymmetric and symmetric stretching vibrations of methylene (–CH₂) groups originating from the organic cations of the quaternary ammonium salt employed in the organophilization of Cloisite 30B [26,30]. Furthermore, the presence of a band at 1464 cm⁻¹, corresponding to the C–H bending vibrations of the alkylammonium groups, corroborates the successful modification of the clay, confirming its organophilic character.

In the spectral region around 1016 cm⁻¹, a band attributed to the stretching vibrations of Si–O–Si bonds within the silicate framework is observed. Additionally, the absorption bands between 915 and 521 cm⁻¹ correspond to vibrational modes associated with the octahedral sheets of montmorillonite, confirming the presence of aluminosilicate components characteristic of the smectite mineral structure [31,32].

These findings unequivocally validate the mineralogical composition and organophilic nature of Cloisite 30B, as demonstrated by the distinct spectral bands corresponding to both the inorganic clay matrix and the incorporated organic functional groups within its structure.

3.2. Adsorption Results

3.2.1. Influence of pH

The assessment of the solution pH constitutes a critical parameter in elucidating adsorption mechanisms, as it directly impacts the chemical equilibrium of ionizable functional groups on the adsorbent surface. Variations in pH markedly influence the electrostatic interactions between the adsorbent and adsorbate, potentially altering the efficacy of the contaminant removal process [33]. In this investigation, the process efficiency was quantified by the removal percentage (%Remo).

Figure 3 illustrates the experimental results depicting the variation in chlorhexidine removal efficiency as a function of pH.

Within the pH range of 4 to 11, the removal percentage (%Remo) remained consistently stable at 96.67%, demonstrating that pH exerted no significant effect on the efficiency of the chlorhexidine adsorption process.

The lack of significant fluctuations in the %Remo response is further substantiated by the coefficient of variation (CV) analysis, as detailed in

Table 2. Analysis of coefficient of variation of pH test for chlorhexidine.

Variable	Total	Mean	StdDev	Variance	Coef. of Var.	Minimum	Median	Maximum	Mode	No. Mode
%Remo	13	95.641	1.601	2.564	1.67	91.667	96.667	96.667	96.6667	8

. The CV, defined as the ratio of the standard deviation to the mean and expressed as a percentage, serves as a robust statistical metric to assess experimental precision and to compare the relative variability across distinct data sets [34,35,36].

As presented in Table 2, the coefficient of variation (CV) was calculated at 1.67%, reflecting minimal data dispersion and, therefore, high experimental reproducibility. These results confirm that pH variations within the tested range do not significantly impact chlorhexidine adsorption, underscoring the robustness and stability of the adsorption system with respect to this parameter.

3.2.2. Adsorption Tests

Table 3. Matrix used for 2³ factorial design.

N° Tests	t (h)	m (g)	Ci (mmol/L)	t (h)	m (g)	Ci (mmol/L)
1	−1	−1	−1	1	0.3	0.043
2	1	−1	−1	6	0.3	0.043
3	−1	1	−1	1	0.5	0.043
4	1	1	−1	6	0.5	0.043
5	−1	−1	1	1	0.3	0.057
6	1	−1	1	6	0.3	0.057
7	−1	1	1	1	0.5	0.057
8	1	1	1	6	0.5	0.057
9	0	0	0	3.5	0.4	0.051
10	0	0	0	3.5	0.4	0.051
11	0	0	0	3.5	0.4	0.051

details the experimental design matrix employed for the 2³ factorial study, specifying the lower (−1) and upper (+1) levels of the three independent variables investigated—contact time (h), adsorbent mass (mmol), and the initial drug concentration in the solution (Ci).

The experimental outcomes corresponding to the outlined design matrix are summarized in

Table 4. Results of 2³ factorial experimental design for organoclay.

N° Tests	Abs	Cf (mmol/L)	Rem%	qeq (mmol/g)
1	0.0681	0.0025	94.2353	0.0068
2	0.0721	0.0026	93.8967	0.0067
3	0.0728	0.0026	93.8374	0.0040
4	0.0808	0.0029	93.1602	0.0040
5	0.0812	0.0030	94.8146	0.0090
6	0.0695	0.0025	95.5618	0.0091
7	0.0681	0.0025	95.6512	0.0055
8	0.0662	0.0024	95.7725	0.0055
9	0.0703	0.0026	94.9825	0.0061
10	0.0711	0.0026	94.9254	0.0061
11	0.0818	0.0030	94.1617	0.0060

, representing the adsorption tests of chlorhexidine conducted according to the 2³ factorial design.

The analysis of the experimental matrix indicates that Test 8 exhibited the highest removal efficiency (%Remo), achieving 95.77%. This optimum performance was recorded under the conditions of maximum adsorbent mass (0.5 g), elevated initial chlorhexidine concentration (0.57 mmol/L), and extended contact time (6 h). Conversely, Test 4, conducted with the same adsorbent mass and contact time but a substantially lower initial drug concentration (0.043 mmol/L), yielded a %Remo of 93.16% and an adsorption capacity (q_eq) of 0.0040 mmol/g. Despite the high removal percentage, this was the lowest among the eight experimental runs, underscoring the significant influence of the initial chlorhexidine concentration on the process’s performance.

To rigorously validate this observation, a comprehensive statistical analysis was performed, evaluating both main effects and interactions among the studied factors.

Regarding the equilibrium adsorption capacity (q_eq), the highest values were consistently observed at the lowest adsorbent dosage tested (0.1 g). This inverse relationship suggests that the specific adsorption capacity diminishes as the adsorbent concentration increases, a trend well-documented in the literature. Similar behaviors have been reported in studies investigating cephalexin adsorption on activated carbon derived from walnut shells, as well as the adsorption of amoxicillin and other β-lactam antibiotics on biomass-based activated carbons [37,38].

The decrease in adsorption capacity with increasing adsorbent dosage can be attributed primarily to particle agglomeration phenomena at higher concentrations, which reduce the effective surface area accessible for adsorption, thereby compromising process efficiency [39,40,41,42,43,44]. Additionally, an excess of adsorbent may result in a higher proportion of unused active sites, decreasing the amount adsorbed per unit of mass [37].

3.2.3. Statistical Analysis

Chlorhexidine Removal Percentage (%Remo)

Table 5. Effects of 2³ factorial design on percentage of removal as function of organoclay mass, time, and concentration of chlorhexidine.

Source	DOF	SS	Contribution (%)	SS (Adj.)	MS (Adj.)	F-Value	p-Value
Model	8	6.74281	94.14	6.74281	0.84285	4.01	0.215
Linear	3	5.56563	77.70	5.56563	1.85521	8.83	0.103
m	1	0.00271	0.04	0.00271	0.00271	0.01	0.920
t	1	0.00095	0.01	0.00095	0.00095	0.00	0.953
C0	1	5.56197	77.65	5.56197	5.56197	26.48	0.036
2-Factor Interactions	3	1.15503	16.13	1.15503	0.38501	1.83	0.372
$\mathrm{m}·$t	1	0.11627	1.62	0.11627	0.11627	0.55	0.534
$\mathrm{m}·$C0	1	0.44383	6.20	0.44383	0.44383	2.11	0.283
$\mathrm{t}·$C0	1	0.59494	8.31	0.59494	0.59494	2.83	0.234
3-Factor Interactions	1	0.01031	0.14	0.01031	0.01031	0.05	0.845
$\mathrm{m}·$${\mathrm{C}}_0·$t	1	0.01031	0.14	0.01031	0.01031	0.05	0.845
Curvature	1	0.01185	0.17	0.01185	0.01185	0.06	0.834
Error	1	0.42005	5.86	0.42005	0.21003
Total	2	7.16287	100.00

displays the estimated effects derived from the 2³ factorial design, analyzing the percentage removal (%Remo) of chlorhexidine as a function of the following independent variables: adsorbent mass, contact time, and initial drug concentration.

The results demonstrate that, at a 5% significance level (p < 0.05), only the initial concentration factor exerted a statistically significant effect on the response variable. Therefore, it can be concluded that, among the evaluated parameters, the initial concentration of the chlorhexidine solution is the sole factor significantly influencing the adsorption process’s efficiency.

Equation (3) depicts the linear regression model derived from the experimental data, incorporating the coefficients of statistically significant main effects and interactions. This model demonstrated the best fit, as evidenced by the metrics in Table 5, accounting for 77.70% of the observed variability (R² = 0.7770). Additionally, the p-value exceeding 0.05 confirms the model’s adequacy in accurately representing the response behavior.

$$\%\mathrm{R}\mathrm{e}\mathrm{m}\mathrm{o}=97.40-12.8m-0.64t-6.9C_0+0.24m·t+28.6m·C_0+1.67t·C_0-1.44m·t·C_0+0.074CtPt$$

(3)

Figure 4 illustrates a standardized Pareto chart of the effects (initial concentration, mass, and time) on the response (removal efficiency).

As illustrated in Figure 4, the normal probability plot of the effects facilitates the assessment of statistical significance by depicting the standardized effects relative to the reference line of the normal distribution. Effects positioned farther from the zero baseline are deemed statistically significant at the 5% significance threshold.

Within this framework, the initial concentration factor (denoted as “C”) exhibits a positive and statistically significant standardized effect, situated to the right of the reference line. This positioning indicates that an increase in the initial chlorhexidine concentration positively influences the removal percentage.

Figure 5 displays a Pareto chart of standardized effects for the removal percentage (%Remo) response variable.

This graph presents the absolute values of the standardized effects, ordered in descending magnitude, alongside the statistical significance threshold (critical value of 4.303), corresponding to a 5% significance level (α = 0.05). The reference line delineates the cutoff above which effects are deemed statistically significant.

As illustrated in Figure 5, only the effect related to the initial concentration factor (denoted as C) surpasses the significance line, indicating that it is the sole factor exerting a statistically significant influence on the %Remo response in the chlorhexidine adsorption process. This finding aligns with prior statistical analyses and reinforces the conclusion that the initial drug concentration is the primary driver of process efficiency when utilizing Cloisite 30B as the adsorbent.

Adsorption Capacity (q_eq)

Table 6. Effects of factorial design on percentage of removal as function of mass of organoclay, time, and concentration of chlorhexidine.

Source	DOF	SS	Contribution (%)	SS (Adj.)	MS (Adj.)	F-Value	p-Value
Model	8	0.000027	99.99	0.000027	0.000003	4016.40	0.000
Linear	3	0.000027	98.00	0.000027	0.000009	10,496.46	0.000
Mass	1	0.000000	0.00	0.000000	0.000000	0.09	0.795
Time	1	0.000020	72.54	0.000020	0.000020	23,310.49	0.000
Initial Conc.	1	0.000007	25.45	0.000007	0.000007	8178.79	0.000
2-Factor Interactions	3	0.000000	1.38	0.000000	0.000000	147.63	0.007
$\mathrm{m}·$t	1	0.000000	0.00	0.000000	0.000000	0.70	0.492
$\mathrm{m}·$C0	1	0.000000	0.01	0.000000	0.000000	2.52	0.253
$\mathrm{t}·$C0	1	0.000000	1.37	0.000000	0.000000	439.67	0.002
3-Factor Interactions	1	0.000000	0.00	0.000000	0.000000	0.51	0.548
$\mathrm{m}·$${\mathrm{C}}_0·$t	1	0.000000	0.00	0.000000	0.000000	0.51	0.548
Curvature	1	0.000000	0.62	0.000000	0.000000	198.39	0.005
Error	2	0.000000	0.01	0.000000	0.000000
Total	10	0.000027	100.00

details the estimated effects of the 2³ factorial design on the equilibrium adsorption capacity (q_eq) of chlorhexidine, taking into account the following independent variables: adsorbent mass, contact time, and initial solution concentration.

The data indicate that, at a 5% significance level (p < 0.05), both contact time and initial concentration exerted a statistically significant effect on the q_eq response. Therefore, it can be affirmed that these two factors substantially influence the efficiency of chlorhexidine’s adsorption onto Cloisite 30B.

Equation (4) presents the linear regression model incorporating the statistically significant coefficients of the main factors (adsorbent mass, contact time, and initial concentration) as well as their interactions, as detailed in Table 6.

$$\begin{array}{c}{q}_{eq}=0.002811-0.001187m-0.000243t+0.011303C_0+0.000114m·t+0.00268m·C_0-0.000748t·C_0\\ -0.000296m·t·C_0-0.000279CtPt\end{array}$$

(4)

This model exhibited strong predictive capability, accounting for 98.00% of the variability in the experimental data (R² = 0.9800) with a p-value exceeding 0.05, thereby confirming its statistical validity.

Figure 6 depicts a normal probability plot of the standardized effects, enabling the identification of statistically significant factors by examining their deviation from the reference line of the normal distribution.

As illustrated in Figure 6, the factors time (B), initial concentration (C), and their interaction (BC) exhibit substantial deviation from the normal distribution reference line, indicating statistical significance at the 5% confidence level. Notably, the initial concentration (C) exerts a positive effect, positioned to the right of the plot, whereas the time factor (B) displays a negative effect, situated to the left. The interaction term (BC) also demonstrates statistical relevance, underscoring its important contribution to the model’s behavior.

Figure 7 displays a Pareto chart of the effects (initial concentration, time, and adsorbent mass) on the response (adsorption capacity).

In this chart, the standardized effects are arranged in descending order of magnitude and compared against the statistical significance threshold (critical value = 4.3). The bars representing the factors time (B), initial concentration (C), and their interaction (BC) exceed this reference line, confirming their statistical relevance within the model. These findings are consistent with prior analyses and underscore the fact that all three terms significantly impact the adsorption capacity (q_eq) of chlorhexidine onto organoclay.

3.2.4. Adsorption Kinetics

To elucidate the kinetics of RB dye’s adsorption onto bauxite residue, the pseudo-first order (PFO) and pseudo-second order (PSO) models [44,45] were applied. These kinetic models are represented by Equations (5) and (6), shown below.

The application of nonlinear kinetic models, specifically the pseudo-first order (PFO) and pseudo-second order (PSO) models, enabled a quantitative understanding of the adsorption dynamics, as evidenced by the trends illustrated in the kinetic plots (Figure 8).

$$q_{eq}=q_e.ex{p}^{(-k_1.t)}$$

(5)

$$q_{eq}={\displaystyle \frac{k_2.q_e^2.t}{(1+t.k_2.q_e)}}$$

(6)

where k₁: the adsorption rate constant of the pseudo-first order model (min); k₂: the adsorption rate constant of the pseudo-second order model (g/mmol.min); q_e: the amount of drug adsorbed at equilibrium (mmol/g); q_t: the amount of drug adsorbed at time t (mmol/g).

Both kinetic models employed to evaluate the adsorption process exhibited a satisfactory alignment with the experimental data. This quality of fit is evidenced by the congruence between the experimental and modeled curves, as well as by the high coefficients of determination obtained, as detailed below.

Table 7. Parameters obtained for pseudo-first order and pseudo-second order models for kinetic tests.

Models	Equations	Parameters
PFO	$q_{eq}=q_e·ex{p}^{(-K_1·t)}$	qeq (mmol/g) = 0.00207
		k1 (g mmol/min) = 1.32978
		R2 = 0.9966
PSO	$q_{eq}={\displaystyle \frac{K_2·q_e^2·t}{(1+t·K_2·q_e)}}$	qeq (mmol/g) = 0.0021
		k1 (g mmol/ min) = 2514.93365
		R2 = 0.9987

presents the parameters obtained for the kinetic fits of Cloisite 30B.

An analysis of the determination coefficients (R²) presented in Table 7 indicates that, although the differences are subtle, the pseudo-second order (PSO) model offers the best fit to the experimental data. This model implies that the adsorption process involves electron sharing or exchange between the adsorbate and the adsorbent, suggesting a chemisorption mechanism in which chlorhexidine interacts with the active sites of the organoclay through electronic interactions.

Supporting this interpretation, previous studies investigating the adsorption of pharmaceutical compounds such as ibuprofen and ketoprofen onto porous carbonaceous materials have demonstrated the suitability of the pseudo-second order model for describing drug adsorption kinetics [46]. These kinetic findings reinforce the notion that the rate-limiting step is governed by the number of active sites available and the amount of chemical species adsorbed at equilibrium [47].

3.2.5. Adsorption Isotherms

Various isotherm models, namely Langmuir, Freundlich, Sips, Temkin, and Redlich–Peterson, were employed to investigate the interaction mechanisms between the drug and the surface of the organoclay. The corresponding equations underpinning these models are presented below as Equations (7)–(11) [48,49,50].

The Langmuir equation is as follows:

$$q_{eq}={\displaystyle \frac{q_{max}{K}_L{C}_e}{{1+K}_L{C}_e}}$$

(7)

where q_eq: the amount of drug adsorbed per gram of adsorbent at equilibrium (mg/g); q_max: the maximum adsorption capacity (mg/g); k_L: the drug/organoclay interaction constant (L/mg); C_e: the drug concentration at equilibrium (mg/L).

Freundlich isotherm model:

$$q_{eq}=K_F{C}_e^{1/n}$$

(8)

where q_eq: the amount of solute adsorbed (mg/g); C_e: the equilibrium concentration in the solution (mg/L); n: the constant related to surface heterogeneity; K_F: the Freundlich adsorption capacity constant (mg/L), (L/g)⁻¹.

Temkin isotherm model:

$$q_{eq}=Bln\left(A{C}_e\right)$$

(9)

$$B={\displaystyle \frac{RT}{b}}$$

where A: the Temkin equilibrium constant (L/g); b: the Temkin equations constant (L/g); B: the constant of adsorption heat (J/mol).

Redlich–Peterson isotherm model:

$$q_{eq}={\displaystyle \frac{A{C}_e}{1+B{C}_e}}$$

(10)

where A: the Redlich–Peterson isotherm constant (L/g); B: the constant (L/mg); β: the exponent that lies between 0 and 1; C_e: the equilibrium liquid-phase concentration of the adsorbent (mg/L); q_eq: the equilibrium adsorbate loading on the adsorbent (mg/g).

Sips isotherm model:

$$q_{eq}={\displaystyle \frac{q_{max}·K_S·C_e\gamma }{1+K_S·C_e\gamma }}$$

(11)

where q_eq: the amount of drug adsorbed per gram of adsorbent at equilibrium (mg/g); q_max: the maximum adsorption capacity (mg/g); K_S: the equilibrium constant (L/mg) γ; C_e: the drug concentration at equilibrium (mg/L); $\gamma$: the system heterogeneity parameter.

The behavior of the chlorhexidine adsorption system using organoclay in response to variations in drug concentration was assessed through the analysis of adsorption isotherm curves. The experimental data were fitted to nonlinear isotherm models, Langmuir, Freundlich, Sips, Temkin, and Redlich–Peterson, whose respective profiles are illustrated in Figure 9.

The adjustment parameters of the models, obtained by nonlinear regression using OriginPro software, version 8.0^® (OriginLab Corporation, Northampton, MA, USA), are presented in

Table 8. Parameters obtained for nonlinear Langmuir, Freundlich, Temkin, Redlich–Peterson, and Sips equations for chlorhexidine.

Model	Parameters	Chlorhexidine
Langmuir	KL (L/mmol)	1.6316 ± 0.1941
	qmax (mmol/g)	3.376 ± 0.3020
	R2	0.99888
Freundlich	KF (mmol/g)	3.2954 ± 0.1652
	n	1.1517 ± 0.0379
	R2	0.99578
Temkin	A (L/g)	67.8213 ± 1.6884
	B	0.2928 ± 0.0035
	R2	0.87467
Redlich–Peterson	A (L/g)	4.02909 ± 0.0264
	B (L/mg)	221.85496 ± 0.0000
	R2	0.76302
Sips	KS (L/mg)γ	4.2925 ± 0.1193
	qmax (mmol/g)	2.0927 ± 0.1193
	γ	1.1491 ± 0.0257
	R2	0.99977

.

The analysis of the experimental data revealed that the Sips isotherm model provided the best fit for chlorhexidine adsorption, exhibiting the highest coefficient of determination (R² = 0.9998) and coming closest to unity out of all the models assessed. The excellent agreement of the Sips model underscores the heterogeneous nature of the adsorbent surface and indicates that the adsorption mechanism predominantly involves monolayer formation, wherein each active site accommodates a single adsorbate molecule [51,52].

Furthermore, the Sips model parameter n > 1n > 1, along with the separation factor R_L within the range of 0 to 1, indicates that the adsorption of chlorhexidine onto the organoclay was favorable across the investigated concentration range. These parameters highlight the system’s high affinity and efficiency during drug removal, even at low initial concentrations, thereby reinforcing the potential of organoclay as an effective adsorbent for the remediation of aqueous solutions contaminated with bioactive compounds.

3.2.6. Efficacy of Adsorbents During Chlorhexidine Removal

In this study, organoclay was employed as a low-cost adsorbent, positioning itself as a more economically viable alternative to advanced materials such as ZIF-8 and Fe₃O₄@graphene nanoplatelet nanocomposites [53,54]. The adoption of organoclay enabled a notable reduction in overall process costs without significantly compromising adsorption performance under the defined operating conditions. To contextualize and benchmark the adsorption efficiency of the developed material,

Table 9. Efficacy of adsorbents during chlorhexidine removal.

Adsorbents	Experimental Conditions T = 25 °C	qmax (mg/g)	Ref.
Organoclay	A = 200 rpm t = 10 min C0 = 0.05 mmol/L m = 1.5 g	4.590	This work
ZIF-8	A = 200 rpm t = 1 h C0 = 0.07 mmol/L m = 0.04 g	30.96	[53]
Fe3O4@graphene nanoplatelets nanocomposite	Ibuprofen	7.630	[54]
	Ketoprofen	8.760	[54]
	Naproxen	10.06	[54]
	Diclofenac sodium salt	14.30	[54]

provides a comparative overview of the maximum adsorption capacities (q_max) reported in the recent literature.

Organoclay, a hybrid phyllosilicate material modified to gain organophilic properties, exhibited a modest adsorption capacity (q_max = 4.590 mg/g) under rapid equilibrium conditions (10 min). Its performance suggests strong potential for fast adsorption scenarios in low-concentration systems.

ZIF-8 (Zeolitic Imidazolate Framework-8), a metal–organic framework with intrinsic microporosity and high surface area, demonstrated superior performance with qmax = 30.96 mg/g. This highlights its structural affinity and adsorptive superiority under prolonged contact times (1 h), even at relatively low mass loading.

The Fe₃O₄@Graphene Nanocomposite, tailored for multi-pollutant removal, showed variable performance across different pharmaceutical contaminants. Its highest adsorption capacity was recorded for Diclofenac sodium (14.30 mg/g), underscoring its relevance in treating complex wastewater matrices with diverse emerging contaminants.

From a process engineering perspective, this comparative framework supports strategic decision-making during the design of advanced adsorption systems, particularly when balancing response time, adsorbent dosage, and target compound specificity. It underscores the necessity of aligning material selection with contaminant profiles and operational targets—a cornerstone in the customization of water purification solutions under emerging circular economy paradigms.

4. Conclusions

Adsorption was evaluated by multifactorial design, considering variables such as pH, contact time, dosage, and initial concentration. High removal efficiencies were obtained throughout the pH range (maximum of 95.77%), with rapid equilibrium and kinetic adjustment to the pseudo-second order model. The equilibrium data best fitted the Sips model, indicating surface heterogeneity and mono- and multilayer adsorption.

Organoclay showed advantages over conventional adsorbents, such as high efficiency, structural stability, and consistent performance under different conditions. Its scalable synthesis and tunable surface chemistry reinforce its potential for the removal of bioactive micropollutants such as chlorhexidine. Future studies should address regeneration, long-term stability, and life cycle analyses.