Adsorption Performance and Modeling of Pb(II) on Magnetically Functionalized TiO₂ Nanoflowers

Abstract

Heavy metal contamination, particularly lead, poses significant environmental and health risks. In this study, a multifunctional TiO₂@PLDOPA@Fe₃O₄ (TPF) nanocomposite was synthesized and evaluated as a reusable adsorbent for lead ion (Pb(II)) removal from aqueous solutions. Batch adsorption experiments were conducted to examine the effects of contact time, temperature, solution pH, adsorbent dosage, and shaking speed on adsorption performance. A high Pb(II) removal efficiency of 84% and an equilibrium adsorption capacity of 72.38 mg g⁻¹ were obtained under optimized conditions. Kinetic analysis revealed that Pb(II) adsorption followed a pseudo-second-order model, indicating surface-controlled interactions. Thermodynamic analysis suggested a spontaneous and endothermic adsorption process dominated by physical interactions and electrostatic attraction Equilibrium data were better fitted by the Freundlich model, suggesting heterogeneous multilayer adsorption on the functionalized composite surface. The maximum monolayer adsorption capacity of TPF reached 263.16 mg g⁻¹, exceeding those of pristine TiO₂ and Fe₃O₄. Regeneration studies showed that the TPF nanocomposite retained approximately 87% of its initial adsorption capacity after five adsorption-desorption cycles, demonstrating good stability and reusability. The integration of hierarchical TiO₂, magnetic Fe₃O₄, and bio-inspired PLDOPA functionalization provides a promising and sustainable strategy for heavy metal removal and highlights the potential of multifunctional nanocomposites in circular and resource-efficient water treatment systems.

1. Introduction

Heavy metal accumulation in water resources is a critical form of pollution, posing significant risks to both ecosystems and human health. Lead is particularly hazardous because of its low degradability, high mobility, and strong bioaccumulation in biological tissues. These characteristics are associated with serious health risks, including neurological disorders and renal dysfunction, and may also induce ecological imbalance. The World Health Organization has set a maximum permissible limit of 10 µg L⁻¹ for Pb(II) in drinking water, highlighting the urgent need for effective remediation strategies [1,2,3].

Conventional removal methods, including chemical precipitation, membrane filtration, and ion exchange, are widely applied. However, they suffer from limitations such as high operational costs, limited selectivity, energy-intensive processes, and secondary pollutant generation. Adsorption has emerged as a promising and sustainable alternative due to its high removal efficiency, low chemical consumption, minimal sludge production, and adaptability to a wide range of contaminants [4,5,6]. The performance of adsorption strongly depends on the properties of the adsorbent. Ideally, an adsorbent should possess a high surface area, abundant functional groups, rapid adsorption kinetics, easy separation capability, and reusability to support sustainable water treatment. Nevertheless, challenges related to adsorbent regeneration, reusability, and stability under repeated adsorption–desorption cycles remain critical for practical applications [7,8].

Magnetic nanoadsorbents have attracted increasing attention because of their rapid magnetic separation and excellent reusability. Fe₃O₄ nanoparticles exhibit strong magnetic responsiveness but often suffer from poor surface stability and limited functionalization. Stabilizing Fe₃O₄ through surface modification and integrating them with functional group-rich architectures has therefore become an important research direction [9,10].

Hierarchically structured TiO₂ nanoflowers are particularly attractive as adsorption platforms due to their three-dimensional flower-like architecture. This structure provides a high specific surface area, abundant exposed active sites, and enhanced mass transfer pathways compared to conventional nanoparticles or nanorods [11,12,13]. However, their nanoscale dimensions make post-treatment recovery challenging and limit their practical applicability in water treatment processes [9,14].

Magnetic functionalization offers an effective solution by enabling rapid and energy-efficient separation using an external magnetic field, reducing the need for filtration or centrifugation [15,16]. Moreover, coupling TiO₂ nanoflowers with magnetic nanoparticles integrates the structural advantages of hierarchical TiO₂ with the operational benefits of magnetic separation. As a result, a multifunctional platform with potentially improved performance compared to single-component nanostructures can be achieved for scalable and more sustainable water purification applications [17,18].

Previous studies have explored TiO₂- and Fe₃O₄-based nanostructures and levodopa (LDOPA, 3,4-dihydroxy-L-phenylalanine) coatings individually for Pb(II) adsorption [10,17,19,20,21]. However, reports on multicomponent hybrid systems integrating hierarchical TiO₂ nanoflowers with LDOPA-mediated Fe₃O₄ immobilization are still limited, and the synergistic effects of these components on adsorption performance and practical sustainability have not been fully clarified.

In this study, a multifunctional TiO₂@PLDOPA@Fe₃O₄ (TPF) nanocomposite was developed by integrating hierarchical TiO₂ nanoflowers, PLDOPA surface functionalization, and magnetically recoverable Fe₃O₄ nanoparticles. The main contributions include: (i) stabilizing the TiO₂ nanoflower architecture through polymerized LDOPA (PLDOPA) coating to enhance surface functionality and structural robustness; (ii) improving Pb(II) complexation via catechol and amine groups of PLDOPA, which also serve as anchoring sites for Fe₃O₄ immobilization; and (iii) enabling rapid magnetic separation through controlled distribution of Fe₃O₄ nanoparticles, supporting efficient material recovery.

The composite was synthesized and characterized in terms of structural and surface properties, and the effects of key experimental parameters on Pb(II) removal were investigated. Adsorption mechanisms were analyzed using isotherm and kinetic models, and regeneration and reusability were evaluated to assess its potential for sustainable water treatment applications.

2. Materials and Methods

2.1. Chemicals and Characterization

All chemicals were of analytical grade and purchased from Sigma-Aldrich (St. Louis, MO, USA). Titanium butoxide, acetic acid, ethanol, FeCl₃·6H₂O, urea, succinic acid, propylene glycol, Tris buffer, tetramethylammonium hydroxide (TMAOH), LDOPA, Pb(NO₃)₂, EDTA (disodium salt), HCl, and NaOH were used in synthesis and adsorption experiments. Deionized water was used in all experiments.

The synthesized nanomaterials were characterized before and after Pb(II) adsorption. Morphology and structure were analyzed using transmission electron microscopy (TEM, Hitachi HighTech HT7700, Tokyo, Japan), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS, Zeiss Sigma 300, Oberkochen, Germany), and X-ray diffraction (XRD, PANalytical Empyrean, Almelo, The Netherlands). Pb(II) concentrations in solution were determined by inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7800, Santa Clara, CA, USA). Particle size distributions were evaluated from SEM and TEM images using ImageJ software (National Institutes of Health, Bethesda, MD, USA), version 1.53, with at least 200 particles measured per sample to ensure statistical reliability.

2.2. Adsorbent Synthesis

2.2.1. TiO₂ Nanoflowers

TiO₂ nanoflowers were synthesized via hydrothermal treatment. Titanium butoxide (1 mL) was added to acetic acid (30 mL) and stirred for 10 min to obtain a homogeneous suspension. The mixture was transferred to a 50 mL Teflon-lined autoclave and heated at 140 °C for 14 h. The precipitate was collected by centrifugation at 9000 rpm for 1 h, washed with deionized water and ethanol, and dried at 60 °C for 12 h. Finally, the dried sample was calcined at 700 °C for 1 h under nitrogen to complete crystallization [11,12].

2.2.2. Fe₃O₄ Nanoparticles

Fe₃O₄ nanoparticles were synthesized via the solvothermal method. FeCl₃·6H₂O (3 mmol), urea (30 mmol), and succinic acid (1 mmol) were dissolved in propylene glycol (30 mL) and transferred to a Teflon-lined autoclave. The mixture was heated at 200 °C for 12 h. Particles were separated magnetically, washed once with deionized water and three times with ethanol, and stored in 0.1 M TMAOH at room temperature (RT) [20,22]. TMAOH was used as a stabilizing medium to prevent Fe₃O₄ oxidation and aggregation. The alkaline environment suppresses the oxidation of Fe²⁺ in magnetite to γ-Fe₂O₃ during storage. TMA⁺ ions adsorb on the nanoparticle surface, providing electrostatic and partial steric stabilization and improving colloidal dispersion. Prior to composite fabrication, the nanoparticles were separated from the TMAOH medium and washed with deionized water and ethanol to remove excess TMAOH [23,24].

2.2.3. TPF Nanocomposite

TiO₂ (6 mg/15 mL) and Fe₃O₄ (2 mg/15 mL) suspensions were prepared in 10 mM Tris’s buffer (pH 8.5). The TiO₂/Fe₃O₄ ratio was selected based on preliminary experiments and previous studies to balance adsorption capacity and magnetic separability [20,25]. The selected Fe₃O₄ content corresponds to ~25 wt%, which is commonly used in magnetic nanocomposites to ensure magnetic separability while maintaining high surface area [15,24]. The suspensions were combined, and 6 mg LDOPA was added. The mixture was shaken at 200 rpm for 3 h to allow LDOPA polymerization and surface functionalization to proceed. The resulting TPF nanocomposite was collected by centrifugation at 1000 rpm for 10 min [20,25].

2.3. Adsorption Experiments

Batch adsorption was performed using laboratory-prepared Pb(II) solutions. The effects of contact time (0–9 h), temperature (25–45 °C), shaking speed (50–200 rpm), solution pH (2–7), and adsorbent dosage (10:1–1000:1 adsorbent:adsorbate, mass ratio) were evaluated.

The effect of temperature was analyzed by determining the distribution coefficient ($K_d$) and calculating Gibbs free energy ($\Delta G°$), enthalpy ($\Delta H°$), and entropy ($\Delta S°$) using the Van’t Hoff approach.

During the evaluation of each parameter, all other experimental conditions were kept constant. Except for the pH studies, all experiments were conducted at the natural pH of the Pb(II) solution. The fixed conditions were as follows: temperature of 25 °C, shaking speed of 150 rpm, adsorbent amount of 25 mg, solution volume of 50 mL, and contact time of 3 h. All experiments were performed in a temperature-controlled thermostatic shaker under dark conditions to avoid potential light-induced effects.

A Pb(II) stock solution (50 mg L⁻¹) was prepared using Pb(NO₃)₂ as the Pb(II) source. The actual Pb(II) concentrations of the stock solution were verified by ICP-MS, and the measured values were used for all calculations. pH adjustments were made using 0.1 M HCl or NaOH. After adsorption, the magnetic adsorbent was separated using an external magnet, and residual Pb(II) concentrations were measured by ICP-MS. The Pb(II)-loaded adsorbent was denoted as TPFP throughout this study.

2.3.1. Adsorption Efficiency and Capacity

Pb(II) removal efficiency ($R$, %) and adsorption capacity ($q_t$, mg g⁻¹) at time $t$ were calculated using Equations (1) and (2), respectively:

$$R(\%)={\displaystyle \frac{{(C}_0-C_t)\times 100}{C_0}}$$

(1)

$$q_t={\displaystyle \frac{{(C}_0-C_t)V}{m}}$$

(2)

where $C_0$ and $C_t$ (mg L⁻¹) represent initial and time-$t$ Pb(II) concentrations, respectively, $V$ (L) is solution volume, $m$ (g) is adsorbent mass.

2.3.2. Adsorption Kinetics

Kinetic experiments were performed at 50 mg L⁻¹ initial Pb(II) concentration. Samples were withdrawn at 0.5, 1, 2, 4, 6, 7, 8, and 9 h. Remaining Pb(II) concentrations were analyzed by ICP-MS. Data were fitted to pseudo-first-order (PFO), pseudo-second-order (PSO), film diffusion (FD), and intraparticle diffusion (IPD) models.

2.3.3. Adsorption Isotherms

Equilibrium adsorption was evaluated at initial Pb(II) concentrations of 10, 25, 50, and 75 mg L⁻¹. Data were analyzed using Langmuir and Freundlich isotherm models.

2.4. Desorption and Reusability Experiments

Desorption and reusability experiments were conducted to evaluate the regeneration performance of the TPF nanocomposite. After each adsorption experiment, the Pb(II)-loaded adsorbent was magnetically separated and rinsed with deionized water to remove residual solution.

Desorption was carried out by contacting the recovered adsorbent with 10 mL of 0.01 M EDTA solution (disodium salt). The mixture was shaken continuously at 200 rpm and RT for 1 h. After desorption, the adsorbent was subsequently separated, and the desorbed Pb(II) concentration in the eluate was determined using ICP-MS.

The regenerated adsorbent was then washed three times with deionized water to remove residual EDTA, dried at 60 °C for 12 h, and reused in subsequent adsorption cycles. Adsorption–desorption experiments were repeated for five consecutive cycles under identical conditions. To ensure a constant adsorbent dosage, all cycles were performed using a fixed adsorbent mass of 25 mg, obtained from parallel experiments conducted under identical conditions.

The desorption efficiency ($DE, \%$) was calculated using Equation (3):

$$DE \left(\%\right)={\displaystyle \frac{q_{des}}{q_{ads}}} \times 100$$

(3)

where $q_{des}$ (mg g⁻¹) and $q_{ads}$ (mg g⁻¹) represent the desorbed and initially adsorbed Pb(II) amounts per unit mass of adsorbent, respectively.

The regeneration efficiency ($RE$, %) was determined using Equation (4):

$$RE \left(\%\right)={\displaystyle \frac{q_n}{q_i}} \times 100$$

(4)

where $q_n$ (mg g⁻¹) is the adsorption capacity after each cycle and $q_i$ (mg g⁻¹) is the initial adsorption capacity.

3. Results and Discussion

3.1. Characterization

3.1.1. XRD Analysis

The crystalline structures of Fe₃O₄, TiO₂, TPF, and TPFP nanomaterials were investigated by XRD, and the corresponding patterns are presented in Figure 1.

For Fe₃O₄ nanoparticles, characteristic diffraction peaks were observed at 2θ values of 18.29°, 30.10°, 35.42°, 43.04°, 53.50°, 56.98°, and 62.60°. These peaks correspond to the (111), (220), (311), (400), (442), (511), and (440) crystal planes, respectively. The diffraction pattern shows good agreement with the standard JCPDS card No. 19–0629 [26], suggesting that the synthesized particles possess a pure magnetite phase with a cubic spinel crystal structure. In particular, the high intensity of the (311) reflection indicates that the spinel structure is the dominant crystalline phase [21,27].

The XRD pattern of TiO₂ nanoflowers exhibits diffraction peaks at 2θ values of 25.4°, 38.0°, 48.2°, 54.0°, 55.2°, 62.8°, 69.0°, 70.5°, and 75.2°, which can be indexed to the (101), (004), (200), (105), (211), (204), (116), (220), and (215) planes of the anatase phase (JCPDS card No. 21–1272) [28]. The peak positions and their full width at half maximum values indicate that the TiO₂ nanoflowers crystallized in a tetragonal crystal system with nanoscale crystallite dimensions [29].

For the TPF nanocomposite, the XRD pattern retains the characteristic diffraction peaks of both Fe₃O₄ and TiO₂ without any noticeable peak shift, broadening, or the appearance of additional phases. This observation confirms that both components were successfully immobilized within the PLDOPA matrix while preserving their original crystal structures. The absence of distinct diffraction peaks associated with PLDOPA is attributed to its amorphous nature. This behavior has been widely reported for polymer-based nanocomposites, where the amorphous polymer phase is masked by the strong diffraction signals of crystalline metal oxide components [21,30,31,32]. The relatively broad diffraction peaks further suggest that the constituent nanoparticles possess small crystallite sizes and maintain a nanocrystalline structure [33].

In the XRD pattern of the TPFP nanomaterial, no additional diffraction peaks corresponding to Pb-containing crystalline phases were detected. This can be attributed to the low Pb content, the predominance of surface-bound Pb(II) species in ionic or amorphous forms, and the masking of weak Pb-related signals by the intense diffraction peaks of TiO₂ and Fe₃O₄ [34]. These results indicate that the crystalline framework of the nanocomposite remains intact after adsorption, indicating good structural stability.

3.1.2. SEM, TEM and EDS Analysis

The morphologies of TiO₂, Fe₃O₄, TPF, and TPFP nanomaterials were investigated using SEM and TEM, as shown in Figure 2 and Figure 3.

SEM images presented in Figure 2 reveal that the TiO₂ nanoflowers consist of three-dimensional hierarchical flower-like architectures. These structures are composed of densely packed nanospike arrays that are uniformly distributed across the surface. Such hierarchical architectures are known to enhance mass transfer and provide abundant active sites, which is advantageous for adsorption-driven water remediation systems [12]. Since Fe₃O₄ nanoparticles could not be clearly distinguished at the SEM scale, their detailed morphological features were further examined by TEM.

EDS analyses (Figure 2a₃–c₃) confirm the presence and expected distribution of Ti, O, Fe, and C elements on the nanocomposite surface. In particular, the presence of hemispherical features on the nanospike structures in Figure 2b₃ indicates the successful immobilization of magnetite nanoparticles. It should be noted that EDS provides local surface composition rather than bulk stoichiometry. The relatively higher Fe atomic percentage observed in the TPF sample can be attributed to the preferential surface localization of Fe₃O₄ nanoparticles and the higher X-ray emission efficiency of Fe compared to Ti [21,35]. After adsorption, the detection of Pb signals in the TPFP structure further confirms the effective binding of heavy metal ions onto the TPF nanocomposite surface (Figure 2c₃). The apparent decrease in Fe atomic percentage after Pb(II) adsorption is attributed to surface coverage by Pb, which partially masks the Fe signal [13,15].

TEM analysis reveals that Fe₃O₄ nanoparticles exhibit a quasi-spherical morphology and show a slight tendency toward agglomeration due to magnetic dipole–dipole interactions (Figure 3a_1,2) [15]. Particle size analysis performed using ImageJ software indicates that the Fe₃O₄ nanoparticles are distributed within a size range of 8–62 nm, with an average diameter of 34.18 nm. In addition, the accumulation of nanoparticles around an external magnet (Figure 3c,d) indicates strong magnetic responsiveness of Fe₃O₄.

TEM images of TiO₂ nanoflowers (Figure 3b) further support the SEM observations, clearly displaying hierarchical nanospike assemblies. ImageJ-based measurements show that the overall sizes of the nanoflowers range from 400 to 690 nm, with an average size of approximately 500 nm. TEM images of the TPF nanocomposite (Figure 3c) demonstrate that the TiO₂ surface is conformally coated with a PLDOPA layer without disrupting the original nanoflower topology. Moreover, the homogeneous distribution of magnetite nanoparticles along the surface confirms that the catechol and amine groups of PLDOPA play a crucial role in the controlled immobilization of Fe₃O₄ nanoparticles [20,31]. Following adsorption, TEM images of the TPFP structure (Figure 3d) reveal the accumulation of Pb species in a layer-like manner along the nanoflower architecture. Importantly, the adsorption process does not compromise the integrity of the nanospike framework, indicating good morphological stability after Pb(II) uptake.

Overall, the combined SEM, TEM, and EDS results confirm that Fe₃O₄ nanoparticles are homogeneously and stably immobilized onto PLDOPA-coated TiO₂ nanoflowers. The resulting hierarchical structure, characterized by a high surface area and abundant functional groups, provides a suitable platform for Pb(II) adsorption. In addition, the magnetic nature of the nanocomposite enables efficient magnetic separation, which is advantageous for practical water treatment applications [25].

3.2. Proposed Formation Mechanism of TPF

The proposed formation mechanism of the hierarchical TPF nanocomposite is schematically illustrated in Figure 4. Initially, TiO₂ nanoflowers are synthesized via the hydrothermal method, likely involving anisotropic crystal growth, providing high-energy facets and abundant nucleation sites [11]. Subsequently, Fe₃O₄ nanoparticles synthesized by the solvothermal method are introduced. They are preferentially adsorbed onto the TiO₂ nanoflower spikes, which may be associated with heterogeneous nucleation and interfacial interactions, and a self-assembly process driven by surface functionalization [36].

During LDOPA polymerization in Tris buffer, a conformal PLDOPA layer is formed, which acts as an adhesive interfacial matrix, immobilizing Fe₃O₄ nanoparticles on the TiO₂ surface and suppressing their aggregation. LDOPA oxidative polymerization and subsequent chelation and coordination interactions are believed to facilitate Fe₃O₄ immobilization. The catechol and amine groups of LDOPA can chelate surface Ti and Fe atoms, inducing interfacial coupling and nanoscale surface modification [14,37]. The immobilized Fe₃O₄ nanoparticles introduce secondary nanoscale roughness on the TiO₂ nanospikes, contributing to the hierarchical architecture [35,36].

The hierarchical structure is suggested to be thermodynamically favored due to surface free energy minimization, which is expected to enhance surface area and interfacial activity [14,36,37]. Such hierarchical structures are considered beneficial for enhancing surface area and interfacial activity, which is relevant for sustainable catalytic and environmental applications [5,8].

3.3. Investigation of Adsorption Parameters

3.3.1. Effect of Contact Time

Pb(II) adsorption exhibited a typical two-stage adsorption behavior, consisting of an initial rapid uptake followed by a slower approach to equilibrium (Figure 5a). Within the first 2 h, removal efficiency increased rapidly to 59%, corresponding to an adsorption capacity of 51.38 mg g⁻¹. Equilibrium was reached after approximately 3 h, with the removal efficiency stabilizing at around 84% and $q_e$ reaching 72.38 mg g⁻¹.

This rapid uptake is advantageous for reducing contact time and energy consumption in practical water treatment processes. The rapid initial uptake can be attributed to the high availability of active surface sites and the dominance of external surface adsorption [32]. The hierarchical morphology of TiO₂ nanoflowers and the high density of functional groups (–OH, –NH, and catechol) provided by the PLDOPA coating are further expected to facilitate rapid Pb(II) uptake during the early stage [37,38]. As adsorption proceeds, progressive site occupation suggests that intraparticle diffusion becomes rate-limiting, resulting in a plateau in adsorption capacity after equilibrium is achieved [25,39,40].

3.3.2. Effect of Initial pH

Solution pH plays a critical role in Pb(II) adsorption by influencing both metal speciation and the surface charge of the adsorbent. At pH values below the point of zero charge (pH_pzc), surface functional groups are protonated, leading to electrostatic repulsion between the positively charged adsorbent surface and Pb(II) ions [21,40,41]. In addition, excess H⁺ ions compete with Pb(II) for available adsorption sites, resulting in reduced uptake [32,34]. With increasing pH, deprotonation of –OH and –NH groups occurs, generating a negatively charged surface that enhances electrostatic attraction toward Pb(II) ions [37,42].

In this study, the pH range was limited to 7 because Pb(II) precipitation as Pb(OH)₂ occurs at higher pH values. Such precipitation can interfere with adsorption measurements and lead to misleading removal results [13,32]. As shown in Figure 5b, Pb(II) removal efficiency increased from 32% under acidic conditions to 96% at pH 7, with a maximum adsorption capacity of 83.55 mg g⁻¹. No Pb(OH)₂ precipitation was observed within the investigated pH range, suggesting that Pb(II) removal predominantly occurred via adsorption [10,36]. Notably, the natural pH of the Pb(II) solution (4.86) falls within the effective adsorption range, indicating that pH adjustment is not required under practical operating conditions [25]. This feature is beneficial for reducing chemical consumption and operational costs.

3.3.3. Effect of Adsorbent Dosage

The adsorbent dosage is a critical parameter from both economic and operational perspectives. At a low adsorbent dosage (10:1), a removal efficiency of 84% was achieved, with a high adsorption capacity of 72.38 mg g⁻¹. Increasing the ratio to 50:1 enhanced the removal efficiency to 91%; however, the adsorption capacity decreased markedly to 15.76 mg g⁻¹. Further increasing the ratio to 1000:1 resulted in only a marginal improvement in removal efficiency (95–96%), while the adsorption capacity declined sharply to 0.83 mg g⁻¹ (Figure 5c).

This behavior can be attributed to the increase availability of surface area and active sites at higher adsorbent dosages, which promotes Pb(II) removal [32,34]. At excessive dosages, however, particle aggregation and partial saturation of active sites limit further improvement in removal efficiency, as commonly reported in the literature [17,33]. Based on these findings, an adsorbent-to-adsorbate ratio of 10:1 was considered a suitable condition, offering a favorable balance between high adsorption capacity and economic feasibility [25].

3.3.4. Effect of Shaking Speed

Shaking speed significantly influences adsorption performance by governing mass transfer and ion transport in the solution. Pb(II) adsorption improved as the shaking speed increased from 50 to 150 rpm. In this range, the removal efficiency increased from 74% ($q_t$ = 64.08 mg g⁻¹) to 84% ($q_t$ = 72.38 mg g⁻¹). However, a further increase to 200 rpm resulted in a decrease in removal efficiency to 77%, with a corresponding adsorption capacity of 66.60 mg g⁻¹ (Figure 5d).

This two-stage adsorption behavior can be explained by hydrodynamic effects on mass transfer. Moderate shaking enhances Pb(II) transport toward the adsorbent surface and reduces boundary layer thickness, thereby facilitating adsorption. At higher shaking speeds, excessive turbulence shortens effective contact time and increases particle–particle collisions. This may hinder the efficient utilization of active adsorption sites [19,39,43]. Accordingly, a shaking speed of 150 rpm was selected as a suitable condition for subsequent experiments [25]. Moderate shaking is also favorable for reducing energy consumption in practical water treatment applications.

3.3.5. Effect of Temperature

Temperature is an important parameter influencing adsorption thermodynamics. In this study, the temperature-dependent adsorption behavior of TPF, TiO₂, and Fe₃O₄ was investigated (Figure 6). Increasing temperature enhances ion kinetic energy and collision frequency with active sites, facilitating diffusion into pores and improving removal efficiency. Elevated temperatures also reduce solution viscosity, enhancing mass transfer across the boundary layer [27,44].

Experimentally, the adsorption capacity increased with temperature for all adsorbents, with TPF showing the highest capacity (from 63.40 to 81.58 mg g⁻¹), followed by Fe₃O₄ and TiO₂ (Figure 6a). This behavior indicates that the adsorption process is endothermic in nature [19]. The superior performance of TPF suggests enhanced interactions between Pb(II) ions and surface functional groups, particularly catechol groups in the PLDOPA layer. Similar temperature-dependent adsorption behavior has been reported for metal oxide- and catechol-functionalized adsorbents [25,27,30,37].

Thermodynamic parameters were calculated using temperature-dependent equilibrium data (

Table 1. Thermodynamic parameters for Pb(II) adsorption onto the TPF, TiO₂, and Fe₃O₄ at different temperatures.

Parameters	Temperature	Value
		TPF	TiO2	Fe3O4
$\Delta G°$ (kJ mol−1)	25 °C (298 K)	−19.18	−17.31	−17.81
	35 °C (308 K)	−20.46	−18.10	−18.76
	45 °C (318 K)	−22.20	−19.35	−20.01
$K_d$	25 °C (298 K)	2305	1084	1326
	35 °C (308 K)	2954	1175	1519
	45 °C (318 K)	4431	1509	1934
$\Delta H°$ (kJ mol−1)		25.66	12.94	14.80
$\Delta S°$ (J mol−1 K−1)		150.26	101.27	109.28

). The distribution coefficient ($K_d$) was calculated according to Equation (5):

$$K_d={\displaystyle \frac{q_e}{C_e}}\times 1000$$

(5)

where $q_e$(mg g⁻¹) is the equilibrium adsorption capacity and $C_e$(mg L⁻¹) is the equilibrium concentration of Pb(II) ions in solution. The multiplication factor of 1000 was introduced to account for the density of water and to maintain unit consistency.

The standard $\Delta G°$ was calculated using Equation (6):

$$\Delta G°=-RT ln K_d$$

(6)

where $R$ is the universal gas constant (8.314 J mol⁻¹ K⁻¹) and $T$ is the absolute temperature (K). $\Delta H°$ and $\Delta S°$ were determined using the Van’t Hoff equation (Equation (7)):

$$ln K_d=- {\displaystyle \frac{\Delta H°}{R}} \left({\displaystyle \frac{1}{T}}\right)+ {\displaystyle \frac{\Delta S°}{R}}$$

(7)

The Van’t Hoff plots exhibited good linearity ($R^2$ > 0.90) for all adsorbents (Figure 6b). Negative $\Delta G°$ values indicate spontaneous adsorption, with TPF exhibiting more negative values, suggesting a stronger affinity for Pb(II) [32,45]. The relatively low $\Delta G°$ values suggest that adsorption is mainly governed by physical interactions, accompanied by surface complexation and electrostatic attraction [46]. Positive $\Delta H°$ values confirm the endothermic nature of adsorption, while positive $\Delta S°$ values indicate increased randomness at the solid–solution interface [30,32,47].

Overall, TPF exhibited superior adsorption performance compared to TiO₂ and Fe₃O₄. Its effective adsorption at ambient temperatures highlights its potential for sustainable and energy-efficient water treatment applications.

These results suggest that physical interactions are energetically favored in the adsorption process, and that the overall adsorption behavior should be interpreted in conjunction with kinetic modeling results.

3.4. Adsorption Kinetics

Adsorption kinetics describes the rate at which dissolved species are transported from the solution to the adsorbent surface and bound at the solid–liquid interface. Kinetic analysis provides insight into adsorption mechanisms and rate-controlling steps, which is essential for the design of wastewater treatment systems [27,39,41].

The goodness of fit between experimental data and kinetic models was evaluated using the regression correlation coefficient ($R^2$), root mean square error (RMSE), and chi-square (${\chi }^2$) statistical analyses. Higher $R^2$ values and lower $RMSE$ and ${\chi }^2$ values indicate better model agreement [48,49].

In this study, the adsorption behavior of Pb(II) onto the TPF, TiO₂, and Fe₃O₄ was evaluated using the pseudo-first-order (PFO), pseudo-second-order (PSO), intraparticle diffusion (Weber–Morris, IPD), and film diffusion (FD) kinetic models.

3.4.1. Pseudo-First-Order Kinetic Model

The PFO kinetic model assumes that the adsorption rate is proportional to the number of unoccupied active sites and is described by the Lagergren equation (Equation (8)) [50]:

$$ln\left(q_e-q_t\right)=ln q_e- k_{1}t$$

(8)

where $q_e$ (mg g⁻¹) is the equilibrium adsorption capacity, $q_t$ (mg g⁻¹) is adsorption capacity at time $t$, and $k_1$ (min⁻¹) is the PFO rate constant.

The PFO model describes the initial adsorption stage but often fails to represent the entire adsorption period, especially for heterogeneous surfaces [51]. In this study, the PFO model yielded $q_{e,cal}$ = 54.09 mg g⁻¹, $k_1$ = 10.20 × 10⁻³ min⁻¹ and $R^2$ = 0.89 for TPF (Figure 7a). The calculated $q_e$ value showed poor agreement with the experimental equilibrium capacity ($q_{e,exp}$ = 72.58 mg g⁻¹), indicating that the PFO model does not satisfactorily describe Pb(II) adsorption onto TPF. Similarly lower $R^2$ and higher $RMSE$ and ${\chi }^2$ values for TiO₂ and Fe₃O₄ further confirm the limited applicability of the PFO model (

Table 2. Kinetic parameters calculated from different models for Pb(II) adsorption onto TPF, TiO₂, and Fe₃O₄.

Kinetic Model	Parameters	Value
		TPF	TiO2	Fe3O4
Pseudo-First-Order (PFO)	$q_e$	54.09	11.83	28.93
	$k_1$ × 103	10.20	3.90	9.30
	$R^2$	0.89	0.52	0.63
	$RMSE$	6.37	12.43	7.05
	${\chi }^2$	6.86	37.26	9.15
Pseudo-Second-Order (PSO)	$q_e$	81.30	33.67	62.50
	$k_2$ × 103	0.22	1.50	0.35
	$h$	1.47	1.70	1.38
	$R^2$	0.99	0.99	0.99
	$RMSE$	3.11	1.95	2.82
	${\chi }^2$	1.18	0.92	1.21
Intraparticle Diffusion (Weber–Morris, IPD)	$C$	20.04	17.17	18.18
	$k_{dif}$	2.73	0.83	2.04
	$R^2$	0.93	0.74	0.85
	$RMSE$	4.15	2.72	4.82
	${\chi }^2$	1.61	1.53	3.04
Film Diffusion (FD)	$k_{fd}$ × 103	7.1	12.5	13.4
	$R^2$	0.99	0.95	0.96
	$RMSE$	10.48	3.94	2.92
	${\chi }^2$	17.93	4.53	1.93

).

These results suggest that the adsorption process cannot be adequately explained by a simple first-order mechanism, likely due to the heterogeneous surface and the involvement of multiple adsorption pathways. Comparable deviations from the PFO model have been reported for oxide–polymer hybrid adsorbents with heterogeneous surfaces [52,53,54].

3.4.2. Pseudo-Second-Order Kinetic Model

The PSO kinetic model assumes that the adsorption rate is proportional to the square of the number of available active sites and that chemisorption may be the rate-limiting step [37]. This model, proposed by Ho and McKay [55], is described by Equation (9):

$${\displaystyle \frac{t}{q_t}}= {\displaystyle \frac{1}{k_2{q}_e^2}} + {\displaystyle \frac{t}{q_e}}$$

(9)

where $q_e$ (mg g⁻¹) is the equilibrium adsorption capacity, $q_t$ (mg g⁻¹) is the adsorption capacity at time $t$, and $k_2$ (g mg⁻¹ min⁻¹) is the PSO rate constant.

The initial adsorption rate ($h$) can be calculated using Equation (10):

$$h= k_2{q}_e^2$$

(10)

In this study, the PSO model provided $q_{e,cal}$ = 81.97 mg g⁻¹, $k_2$ = 0.22 × 10⁻³ g mg⁻¹ min⁻¹,$h$ = 1.49 mg g⁻¹ min⁻¹ and an excellent fit ($R^2$ = 0.99) for TPF (Figure 7b). High $R^2$ and low $RMSE$ and ${\chi }^2$ values for TiO₂ and Fe₃O₄ further confirm that PSO adequately describes Pb(II) adsorption kinetics for all adsorbents (Table 2).

The close agreement between calculated and experimental $q_e$ values supports the applicability of the PSO model. The enhanced kinetics of TPF are attributed to catechol groups in the PLDOPA layer, which provide additional active sites [14,35].

These results are consistent with previous studies reporting PSO-dominated kinetics for metal oxide–polymer hybrid adsorbents [27,30,31,56].

3.4.3. Intraparticle Diffusion Model

Adsorption commonly involves external mass transfer followed by intraparticle diffusion. The intraparticle diffusion model is expressed as Equation (11) [57,58]

$$q_t=k_{dif}{t}^{1/2}+C$$

(11)

where $q_t$ (mg g⁻¹) is the adsorption capacity at time $t$, $k_{dif}$ (mg g⁻¹ min⁻¹ᐟ²) is the intraparticle diffusion rate constant, and $C$ (mg g⁻¹) reflects the boundary layer thickness.

In this study, the intraparticle diffusion model yielded $k_{dif}$ = 2.73 mg g⁻¹ min⁻¹ᐟ², $C$ = 20.04 mg g⁻¹, and $R^2$ = 0.93 for TPF. The deviation of the fitted line from the origin and the relatively high $C$ value indicate that boundary layer diffusion contributes significantly (Figure 7c). Similar trends were observed for TiO₂ and Fe₃O₄, suggesting that Pb(II) transport involves both external mass transfer and intraparticle diffusion (Table 2). Comparable observations have been reported for TiO₂—and Fe₃O₄—based composite adsorbents [32,34,46,52].

3.4.4. Film Diffusion Model

The film diffusion model describes mass transfer across the boundary layer surrounding the adsorbent particles (Equation (12)) [59]:

$$ln\left(1-F\right)=-k_{fd}t$$

(12)

where $F$ (=$q_t$/$q_e$) represents the fractional adsorption at time $t$, $q_t$ (mg g⁻¹) is the adsorption capacity at time $t$, $q_e$ (mg g⁻¹) is the equilibrium adsorption capacity, and $k_{fd}$ (min⁻¹) is the fractional diffusion rate constant.

For TPF, $k_{fd}$ = 0.007 min⁻¹ and $R^2$ = 0.99 were obtained in the $F$ < 0.5 region (Figure 7d). The strong linearity in the initial adsorption stage indicates that film diffusion plays a significant role at the early phase of Pb(II) uptake. However, film diffusion is not considered the sole rate-controlling step, as intraparticle diffusion and surface reaction processes are expected to contribute at later stages of adsorption. Similar behavior was observed for TiO₂ and Fe₃O₄, suggesting that external mass transfer resistance dominates during the initial adsorption period, followed by intraparticle diffusion-controlled transport. This adsorption behavior is consistent with previous studies on hybrid and polymer-functionalized nanocomposite adsorbents [45,60,61].

3.4.5. Overall Kinetic Interpretation

Although Pb(II) adsorption kinetics were well described by the PSO model, this model reflects adsorption rate behavior and does not necessarily imply a purely chemisorption-controlled mechanism. Previous studies have shown that the PSO kinetics can describe systems governed by combined physical adsorption and weak surface-related interactions, such as surface complexation or ion exchange [4,62,63]. The relatively low $\Delta G°$ (−19.18 to −22.20 kJ mol⁻¹) suggest that physical interactions are energetically favored [43,54].

Accordingly, Pb(II) adsorption onto TPF can be interpreted as a mixed adsorption mechanism. The PSO behavior suggests the involvement of surface coordination and weak chemical interactions with catechol, amine, and metal oxide hydroxyl groups. Meanwhile, electrostatic interactions and physisorption dominate the adsorption energetics. Kinetic parameters from different models indicate a multi-step adsorption process, supported by the high PSO correlation coefficient ($R^2$ = 0.99), low $RMSE$ and ${\chi }^2$ values, and good agreement between calculated and experimental $q_e$ values [10,13,18,58].

The relatively high initial adsorption rate ($h$ = 1.49 mg g⁻¹ min⁻¹) indicates rapid interaction between Pb(II) ions and surface functional groups [16,18,27,39]. The intraparticle diffusion parameters ($k_{dif}$ = 2.73 mg g⁻¹ min⁻¹ᐟ² and $C$ = 20.04 mg g⁻¹) suggest that adsorption is not governed solely by pore diffusion, while the nonzero $C$ value highlights the contribution of film diffusion. Thus, Pb(II) adsorption can be described as a three-stage process: (i) initial film diffusion, (ii) surface interaction-controlled adsorption, and (iii) gradual intraparticle diffusion [27,61,62].

This kinetic behavior is consistent with the multiphase structure and functional-group-rich surface chemistry of TPF. TiO₂ and Fe₃O₄ provide metal oxide sites for electrostatic attraction and surface coordination, while PLDOPA contributes catechol and amine groups that enhance surface interactions. Compared with TiO₂ and Fe₃O₄, TPF exhibited higher adsorption rates and better model fitting, highlighting the synergistic effect of polymer functionalization [34,35,40].

Overall, Pb(II) adsorption on TPF, TiO₂, and Fe₃O₄ follows a multi-step mechanism. Surface-related interactions mainly influence the adsorption rate, while thermodynamic results indicate that physical interactions are energetically dominant.

3.5. Adsorption Isotherms

Adsorption equilibrium is commonly described using isotherm models to characterize adsorbate–adsorbent interactions under equilibrium conditions. These models provide quantitative information regarding the pollutant removal capacity of an adsorbent under specific system conditions [64,65]. Among the various equilibrium models, the Langmuir and Freundlich isotherms are the most widely employed to interpret adsorption behavior in water and wastewater treatment applications [63].

In this study, both Langmuir and Freundlich models were applied to evaluate the equilibrium adsorption behavior of Pb(II) ions onto the TPF, TiO₂, and Fe₃O₄. Model performance was assessed using $R^2$, $RMSE$, and ${\chi }^2$ statistical analyses [49].

3.5.1. Langmuir Isotherm Model

The Langmuir isotherm assumes monolayer adsorption on an energetically homogeneous surface with a finite number of identical active sites and no lateral interactions between adsorbed species. The model is expressed as Equation (13) [66,67]:

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

(13)

where $q_e$ (mg g⁻¹) is the equilibrium adsorption capacity, $q_{max}$ (mg g⁻¹) is the maximum monolayer adsorption capacity, $C_e$ (mg L⁻¹) is the equilibrium concentration of Pb(II) in solution, and $K_L$ (L mg⁻¹) is the Langmuir constant related to adsorption energy and binding affinity. The $q_{max}$ values are theoretical parameters obtained from model fitting and should not be interpreted as experimentally attained capacities.

Higher values of $q_{max}$ and $K_L$ indicate enhanced adsorption performance. The Langmuir constant $K_L$ reflects the affinity between the adsorbent and the adsorbate and is directly related to the binding strength [30,41].

The dimensionless separation factor ($R_L$), which indicates the favorability of adsorption, is calculated as Equation (14) [68]:

$$R_L={\displaystyle \frac{1}{1+ K_L{C}_0}}$$

(14)

where $C_0$ (mg L⁻¹) represents the initial concentration of Pb(II) ions in the aqueous solution. Values of 0 < $R_L$ < 1 indicate favorable adsorption behavior consistent with the Langmuir model [68].

The Langmuir model yielded $q_{max}$ values of 263.16, 192.31, and 212.77 mg g⁻¹ for TPF, TiO₂, and Fe₃O₄, respectively. The corresponding $K_L$ values were 0.05 L mg⁻¹ for TPF and 0.01 L mg⁻¹ for both TiO₂ and Fe₃O₄, suggesting a stronger binding affinity of Pb(II) ions toward the TPF surface (Figure 8a).

The calculated $R_L$ values (0.31–0.63) confirmed favorable adsorption for all materials. Although the Langmuir $R^2$ values were moderate, the low $RMSE$ and ${\chi }^2$ values indicate acceptable agreement with experimental data, suggesting that monolayer adsorption is a reasonable approximation. The lower $R^2$ values, particularly for TiO₂ and Fe₃O₄, imply that surface heterogeneity contributes to the adsorption behavior (

Table 3. Isotherm parameters calculated from different models for Pb(II) adsorption onto TPF, TiO₂, and Fe₃O₄.

Isotherm Model	Parameters	Value
		TPF	TiO2	Fe3O4
Langmuir	$q_{max}$	263.16	192.31	212.77
	$K_L$	0.05	0.01	0.01
	$R_L$	0.31	0.63	0.60
	$R^2$	0.89	0.68	0.76
	$RMSE$	3.37	3.19	3.58
	${\chi }^2$	1.02	0.96	1.16
Freundlich	$K_F$	14.48	3.05	4.05
	$n$	1.30	1.17	1.22
	$R^2$	0.99	0.97	0.98
	$RMSE$	4.46	5.53	2.95
	${\chi }^2$	1.11	2.18	1.02

) [18,27].

3.5.2. Freundlich Isotherm Model

The Freundlich isotherm is an empirical model describing reversible multilayer adsorption on heterogeneous surfaces and is particularly suitable for functionalized adsorbents [64]. The model is expressed as follows Equation (15) [69]:

$$q_e= K_F{C}_e^{1/n}$$

(15)

where $K_F$ [(mg g⁻¹) (L mg⁻¹)^1/n] is the Freundlich adsorption capacity constant, and n is an empirical parameter related to adsorption intensity and surface heterogeneity. Values of $n$ > 1 indicate favorable adsorption [30].

For the TPF nanocomposite, the Freundlich model provided an excellent fit with $R^2$ = 0.97, $K_F$ = 14.83 (mg g⁻¹) (L mg⁻¹)^1/n, and $n$ = 1.40 (Figure 8b). Similarly, high correlation coefficients were obtained for TiO₂ ($R^2$ = 0.97, $K_F$ = 3.05, $n$ = 1.17) and Fe₃O₄ ($R^2$ = 0.98, $K_F$ = 4.05, $n$ = 1.22), indicating heterogeneous adsorption sites with non-uniform energies.

Although $RMSE$ and ${\chi }^2$ values were comparable to those of the Langmuir model, the higher $R^2$ values suggest that the Freundlich model better describes the equilibrium adsorption behavior, particularly for TiO₂ and Fe₃O₄ (Table 3).

The $n$ values greater than unity suggest that the adsorption process is favorable and predominantly governed by physical interactions. Moreover, the higher $K_F$ value of the TPF reflects its stronger surface affinity and enhanced adsorption capacity, highlighting the synergistic contribution of TiO₂, Fe₃O₄, and PLDOPA functional groups [30,70].

3.5.3. Overall Isotherm Interpretation

The surface of the TPF nanocomposite consists of coexisting TiO₂, PLDOPA, and Fe₃O₄ phases, resulting in a structurally and energetically heterogeneous surface enriched with –OH, –NH, and catechol functional groups. These functional moieties enable multiple Pb(II) binding mechanisms, including surface complexation, electrostatic interactions, and hydrogen bonding [13,71]. Such surface heterogeneity suggests that adsorption cannot be fully described by a single idealized isotherm model [65,66].

Isotherm parameters obtained from all models are presented in Table 3. The Freundlich model exhibited higher correlation coefficients ($R^2$ = 0.99, 0.97, and 0.98 for TPF, TiO₂, and Fe₃O₄, respectively), indicating a better description of adsorption on heterogeneous surfaces with non-uniform binding energies. In contrast, the Langmuir model showed lower $R^2$ values but comparable and relatively low $RMSE$ and ${\chi }^2$ values, suggesting that monolayer adsorption on energetically similar sites also contributes to Pb(II) uptake [46,69].

Overall, the combined isotherm analysis indicates a complex adsorption mechanism in which heterogeneous multilayer adsorption is likely dominant, accompanied by localized monolayer adsorption on specific active sites. These findings are consistent with previously reported Pb(II) adsorption behavior for Fe₃O₄—and TiO₂—based adsorbents [13,16,18,27,40,41].

3.6. Comparative Adsorption Behavior

To facilitate comparison with similar TiO₂—, and Fe₃O₄—based adsorbent systems reported in the literature, a comparative summary of Pb(II) adsorption performance is presented in

Table 4. Comparison of Pb(II) adsorption performances of different adsorbents in the literature.

Adsorbent	R (%)	${\mathit{q}}_{\mathit{e},\mathit{e}\mathit{x}\mathit{p}}$ (mg g−1)	${\mathit{t}}_{\mathit{e}}$ (min)	${\mathit{q}}_{\mathit{m}\mathit{a}\mathit{x}}$ (mg g−1)	Experimental Conditions	Isotherm Model	Kinetic Model	ΔG° (T) (kJ mol−1)	ΔH° (kJ mol−1)	ΔS° (J mol−1 K−1)	Ref.
TiO2	90	197	30	256.41	pH 6.5, 25 °C, 6 h, 170 rpm	Freundlich	PSO	−23.8 (289 K)	-	-	[46]
TiO2	99	47.47	-	65.99	pH 6.5, 25 °C, 1 h, 300 rpm	Langmuir	PSO	-	-	-	[34]
TiO2@hydroxide ethyl aniline	-	26.05	120	24.25	pH 5.5, 25 °C, 3 h, 150 rpm	Langmuir	IPD	−3.145 (298 K) −3.581 (313 K) −4.515 (298 K)	10.380	45	[32]
polythiophene/TiO2	-	113.38	20	163.51	pH 5, 25 °C, 3 h, 200 rpm	Freundlich	PSO	-	-	-	[41]
Fe3O4	96.77	53.53	-	53.37	pH 5, 40 °C, 3 h, 200 rpm	Freundlich	-	−25.14 (293 K) −26.44 (303 K) −26.72 (313 K)	11.04	66.14	[13]
HCO-(Fe3O4)x	96.05	-	240	35.93	pH 4, 30 °C, 5 h, 150 rpm	Freundlich	PSO	-	-	-	[40]
Fe3O4@polydopamine	-	294.4	324	297.2	pH 5.8, 5.4 h	Langmuir	-	-	-	-	[70]
MnO2/PDA@Fe3O4	98.33	236.60	300	295.01	pH 6.0, 25 °C, 24 h, 180 rpm	Langmuir	PSO	−4.59 (198 K) −5.57 (308 K) −6.55 (318 K)	24.61	97.99	[30]
CGMA-MAn-IDAc/Fe3O4-NH2	99.9	53.33	20	101.010	pH 5, 25 °C, 20 min, 100 rpm	Temkin	PSO	-	-	-	[39]
Fe3O4@UiO-66-PDA	-	121.42	360	529.10	pH 5, RT, 20 h	Freundlich	PSO	−5.66 (298 K) −7.27 (308 K) −8.88 (318 K)	42.32	161.00	[27]
Fe3O4-CS-L	-	97.3	45	128.63	pH 6, 25 °C, 45 min	Freundlich	PSO	−0.45 (298 K) −0.69 (303 K) −0.93 (308 K) −1.17 (313 K)	13.96	48.35	[16]
APTS-Fe3O4/APT@CS	99	625.34	90	636.94	pH 6, 20 °C, 2 h, 200 rpm	Langmuir	PSO	−11.16 (293 K) −11.71 (298 K) −11.94 (303 K)	42.86	108.12	[19]
Fe3O4@C@TiO2	92	15.6	180	-	pH 7, 25 °C, 3 h	-	PFO	-	-	-	[29]
Titanate/Fe3O4	90	-	60	382.3	pH 5, 25 °C, 4 h, 200 rpm	Langmuir	PSO	-	-	-	[36]
Fe3O4-TiO2	90	12.94	15	11.23	37 °C, 160 rpm	Freundlich	PSO	−2.39 (305 K) −3.05 (310 K) −3.72 (315 K)	38.27	133.31	[18]
TiO2/SiO2/Fe3O4- polyacrylic acid	95	-	120	42.34	pH 6, 36 h	Langmuir	PSO	-	-	-	[21]
TiO2	36	35.28	-	192.31	25 °C, 3 h, 150 rpm	Freundlich	PSO	−17.31 (298 K) −18.10 (308 K) −19.35 (318 K)	12.94	101.27	This study
Fe3O4	59	57	-	212.77	25 °C, 3 h, 150 rpm	Freundlich	PSO	−17.81 (298 K) −18.76 (308 K) −20.01 (318 K)	14.80	109.28	This study
TiO2@PLDOPA@ Fe3O4	84	72.38	180	263.16	pH 4.3, 25 °C, 3 h, 150 rpm	Freundlich	PSO	−19.18 (298 K) −20.46 (308 K) −22.20 (318 K)	25.66	150.26	This study

. The $q_{max}$ values represent theoretical maximum adsorption capacities obtained from isotherm models and do not correspond to experimentally equilibrium capacities ($q_{e,exp}$). Additionally, the equilibrium time ($t_e$) denotes the contact time required to reach adsorption equilibrium, and the listed experimental conditions correspond to those used for isotherm calculations. Model selection in Table 4 is based on $R^2$ to ensure consistency with previous adsorption studies. $RMSE$ and ${\chi }^2$ values were considered to provide further statistical validation in the kinetic and isotherm analyses.

A comparative evaluation of pristine TiO₂, Fe₃O₄, and the TPF was conducted to elucidate the contribution of each component and to assess the effect of composite formation. As shown in Table 4, TiO₂ and Fe₃O₄ exhibited moderate Pb(II) removal efficiencies of 36% and 59%, with $q_{e,exp}$ values of 35.28 and 57 mg g⁻¹, respectively. In contrast, the TPF nanocomposite achieved a higher removal efficiency (84%) and $q_{e,exp}$ value (72.38 mg g⁻¹), indicating enhanced adsorption performance of the hybrid material. Furthermore, kinetic, isotherm, and thermodynamic analyses consistently indicated superior adsorption behavior of the TPF composite, including higher adsorption capacities, more negative $\Delta G°$, and improved kinetic performance, suggesting a synergistic enhancement compared to pristine components.

Table 4 also compares the Pb(II) adsorption performance of TPF with previously reported TiO₂—, Fe₃O₄—, and hybrid-based adsorbents. The $q_{max}$ value of TPF (263.16 mg g⁻¹) is higher than those of pristine TiO₂ and Fe₃O₄ in this study and comparable to or higher than many reported TiO₂— and Fe₃O₄—based composites, particularly those without polymeric or chelating functional layers. Although some functionalized Fe₃O₄-based systems show higher capacities, they often involve more complex synthesis routes or longer equilibrium times.

Overall, the TPF composite demonstrates a balanced performance with enhanced adsorption capacity, favorable thermodynamic parameters, and relatively short equilibrium times, highlighting the effectiveness of PLDOPA functionalization and the integrated TiO₂/Fe₃O₄ structure. These results position TPF as a competitive and structurally efficient adsorbent for Pb(II) removal.

3.7. Regeneration and Reusability

The regeneration and reusability performance of the adsorbent were evaluated through five consecutive adsorption–desorption cycles to assess its practical applicability for Pb(II) removal. Moreover, the magnetic nature of the TPF nanocomposite enabled rapid separation from the aqueous phase using an external magnetic field, indicating its potential suitability for repeated reuse in water treatment applications. The experimental results are presented in Figure 9.

In the first cycle, the adsorption capacity ($q_{ads}$) was 80.6 mg g⁻¹, while the reusability efficiency ($RE$), desorption efficiency ($DE$), and removal efficiency ($R$) were 99%, 93%, and 86%, respectively. These results indicate that the eluent solution effectively removed Pb(II) ions from the adsorbent surface, enabling efficient recovery of active adsorption sites. As the number of regeneration cycles increased, a gradual decrease in adsorption capacity and efficiency parameters was observed. After the fifth cycle, $q_{ads}$ decreased to 70.36 mg g⁻¹, while $RE$, $DE$, and $R$ values declined to 87%, 69%, and 75%, respectively. Nevertheless, the adsorbent retained approximately 87% of its initial adsorption capacity after five cycles, demonstrating good structural stability and good regeneration capability.

The gradual reduction in removal efficiency suggests partial blockage of active adsorption sites or minor losses of surface functional groups during repeated regeneration processes. This observation indicates that the PLDOPA coating and the TiO₂@Fe₃O₄ core–shell structure remained relatively stable throughout the adsorption–desorption cycles. The observed decrease in adsorption and desorption performance over successive cycles can be attributed to incomplete desorption, mechanical loss of the adsorbent, or irreversible occupation of active sites due to strong complexation of Pb(II) ions, as widely reported for similar bio-based adsorbents [17,27,30,47]. EDTA was selected as the desorbing agent due to its strong chelating affinity toward Pb(II) [10,72,73]. The catechol and amine functional groups derived from PLDOPA in the adsorbent structure can form strong coordination bonds with Pb(II); therefore, EDTA facilitates Pb(II) desorption via competitive complexation, weakening these interactions. The progressive decrease in desorption efficiency with increasing cycle number may be associated with partial irreversible binding of Pb(II) ions to PLDOPA-based functional groups [74,75,76].

Overall, the acceptable adsorption and desorption performance over five regeneration cycles suggests that the developed adsorbent shows promising potential as a reusable and cost-effective material for Pb(II) removal, contributing to sustainable wastewater treatment strategies and circular material utilization.

4. Conclusions

This study demonstrates that the TiO₂@PLDOPA@Fe₃O₄ (TPF) nanocomposite is an efficient and reusable adsorbent for Pb(II) removal, contributing to sustainable water treatment technologies. The integration of hierarchical TiO₂ nanoflowers, PLDOPA surface functionalization, and magnetically recoverable Fe₃O₄ nanoparticles significantly enhanced adsorption performance compared to pristine components. It also demonstrated the importance of multifunctional hybrid materials in environmental remediation.

The adsorption process exhibited rapid kinetics and favorable thermodynamic behavior, with pseudo-second-order kinetics and Freundlich isotherm models providing the best fit to experimental data. These results indicate a heterogeneous adsorption mechanism involving electrostatic attraction, surface complexation, and multilayer adsorption. The high theoretical adsorption capacity and strong binding affinity highlight the effectiveness of PLDOPA-derived catechol and amine groups in improving Pb(II) uptake.

From a sustainability perspective, the magnetic separability and high regeneration efficiency of the TPF nanocomposite support its practical applicability and resource-efficient reuse. The adsorbent retained approximately 87% of its initial capacity after five cycles, demonstrating good structural stability and potential for repeated application with reduced material consumption and waste generation.

Overall, the TPF nanocomposite represents a promising and sustainable adsorbent platform for heavy metal remediation. Its scalable synthesis, high performance, and reusability make it a viable candidate for integration into advanced water treatment systems, contributing to circular material utilization and the protection of water resources and human health.