Electrostatic Interactions Override Surface Area Effects in Size-Dependent Adsorptive Removal of Microplastics by Fe₃O₄ Nanoparticles

Abstract

Microplastics (MPs), as an emerging persistent contaminant, pose a potential threat to ecosystems and human health. The adsorptive removal of MPs from aqueous environments using magnetic nanoparticles has become a particularly promising remediation technology. Nevertheless, there remain significant knowledge gaps regarding its adsorption mechanism, especially how the key physical properties of magnetic nanoparticles regulate their adsorption behavior towards MPs. This study first investigated the relationship between the particle size of Fe₃O₄ nanoparticles and their adsorption efficacy for MPs. The results demonstrated a non-monotonic, size-dependent adsorption of MPs by Fe₃O₄ nanoparticles, with the adsorption efficiency and capacity following the order: 300 nm > 15 nm > 100 nm. This non-linear relationship suggested that factors other than specific surface area (which would favor smaller particles) are significantly influencing the adsorption process. Isotherm analysis indicated that the adsorption is not an ideal monolayer coverage process. Kinetic studies showed that the adsorption process could be better described by the pseudo-second-order model, while intra-particle diffusion played a critical role throughout the adsorption process. Furthermore, the effect of pH on adsorption efficiency was examined, revealing that the optimal performance occurs under neutral to weak acidic conditions, which is consistent with measurements of surface charges of nanoparticles. These findings suggest that the adsorption is not determined by specific surface area but is dominated by electrostatic interactions. The size-dependent adsorption of MPs by Fe₃O₄ nanoparticles provides new insights for the modification of magnetic adsorbents and offers a novel perspective for the sustainable and efficient remediation of environmental MPs pollution.

1. Introduction

Global plastics production has grown dramatically over the past seven decades, reaching over 390.7 million tonnes in 2021 [1], yet the amount of recycled plastic waste remains limited. For instance, a mere 29.5 million tonnes of post-consumer plastic waste was collected in the EU27 + 3 region in 2020 [2]. The large-scale production and limited recycling of plastics have resulted in the substantial release of plastics into the environment. These plastics undergo fragmentation and degradation into smaller particles, eventually forming microplastics (MPs) with sizes less than 5 mm. Due to their small size and high stability [3], MPs are readily dispersed, persist in the environment [4] and have posed serious threats to ocean health [5]. They may be accidentally ingested by aquatic and terrestrial organisms, accumulating through food chains and potentially inducing physical damage and physiological toxicity [6]. In addition, the large specific surface area of MPs enables them to adsorb environmental contaminants like persistent organic pollutants (POPs), heavy metal ions, and pathogenic microorganisms, resulting in a “Trojan horse” effect [7]. This phenomenon facilitates the transfer of harmful substances to organisms, amplifying their toxic impacts and potentially threatening ecosystems and human health [8]. Recent studies have demonstrated that MPs have been detected in human blood, lungs, placentas and even brain tissue in autopsy samples [9,10,11,12]. Although understanding of the health effects of MPs is still in its infancy, existing studies have indicated that their accumulation may induce inflammation, oxidative stress, immunosuppression, endocrine disruption, as well as potential oncogenic and cardiovascular risks [13,14,15]. Hence, effective separation and recycling technologies are needed to address the issue of MPs pollution.

A variety of physical (e.g., filtration, sedimentation, flotation and adsorption), chemical (e.g., coagulation, flocculation, oxidation, pyrolysis and electrocoagulation) and biological (e.g., activated sludge treatment, aerobic and anaerobic digestion, and membrane bioreactors) techniques have been employed in MPs removal [16,17,18,19,20,21]. Cost, efficiency, and sustainability are the main limitations of widespread application of these removal technologies [22]. Adsorption is considered a highly promising approach for removing MPs due to its simple operation, relatively low cost, wide applicability, and high removal efficiency for target pollutants [23]. A wide range of adsorbents such as sponges, activated carbon, biochar and graphene, have been studied for MPs adsorption [24,25]. In recent years, magnetic nanomaterials, particularly Fe₃O₄ nanoparticles, have attracted considerable attention [26,27]. Fe₃O₄ nanoparticles offer excellent adsorption performance as a result of high specific surface area and abundant surface-active sites [28]. More importantly, super-para-magnetism allows the Fe₃O₄ nanoparticles to be rapidly separated from water via an external magnetic field [29]. Also, the recycling of Fe₃O₄ nanoparticles can be achieved by changing pH and the addition of reagents [30,31]. Some studies have explored the application of Fe₃O₄ nanoparticles for MPs removal [32,33,34], the adsorption of MPs by Fe₃O₄ nanoparticles is influenced by environmental conditions, the properties of MPs, and Fe₃O₄ nanoparticles [35,36]. The particle size of Fe₃O₄ nanoparticles is a particularly critical parameter, as it directly affects properties such as specific surface area, surface energy, and magnetic responsiveness [37]. Fe₃O₄ nanoparticles size has also been found to have a significant impact on adsorption [38,39]. For instance, Shen et al. [40] prepared Fe₃O₄ nanoparticles with different particle sizes to remove heavy metal ions from contaminated water. They found that nanoparticles with an average diameter of 8 nm exhibited an adsorption capacity nearly seven times higher than that of coarse particles, owing to their high specific surface area. Lin et al. [41] reported that the synthesized Fe₃O₄ nanoparticles removed Reactive Red 2 far more efficiently than commercially obtained Fe₃O₄ nanoparticles, due mainly to the smaller size and larger surface charge. Luo et al. [42] demonstrated that smaller Fe₃O₄ nanoparticles carry a higher density of surface functional groups, facilitating tetracycline adsorption. Ulusal et al. [43] investigated the adsorption of dye by Fe₃O₄ nanoparticles with two pore sizes and conducted reusability experiments over five cycles via chemical washing, finding that the removal efficiency of small-pore Fe₃O₄ nanoparticles was consistently higher than that of large-pore nanoparticles in every cycle. The main interactions between MPs and Fe₃O₄ nanoparticles include hydrogen bonds, electrostatic interactions, hydrophobic interactions and π-π interactions [44,45]. To the best of our knowledge, systematic research data on the influence of size of Fe₃O₄ nanoparticles on MPs adsorption remains scarce.

The objective of this research is to investigate the adsorption behavior and interaction mechanisms of MPs on Fe₃O₄ nanoparticles of varying sizes. In this work, Fe₃O₄ nanoparticles with three sizes are used to remove six common MPs from water, including polyethylene terephthalate (PET), polystyrene (PS), polypropylene (PP), low-density polyethylene (LDPE), high-density polyethylene (HDPE), and polyvinyl chloride (PVC). This study hypothesizes that Fe₃O₄ nanoparticles with smaller particle size can achieve superior MPs adsorption performance. The relationship between the particle size of Fe₃O₄ nanoparticles and the adsorption mechanism is further studied by exploring adsorption isotherms and kinetic models for different-sized Fe₃O₄ nanoparticles and evaluating the effects of pH on adsorption. This study provides insights for better understanding of the interactions between Fe₃O₄ nanoparticles and MPs, and for optimizing the design of magnetic nano-adsorbents to achieve efficient removal of multi-component MPs from aqueous environments.

2. Materials and Methods

2.1. Chemicals and Materials

For the magnetic adsorbents, three typical iron oxides with median particle sizes of 15 nm, 100 nm, and 300 nm were supplied by Xingye Metal Material Co., Ltd. (Dongguan, China), Beesley New Materials Co., Ltd. (Suzhou, China), and Kojundo Chemical Laboratory Co., Ltd. (Sakado, Japan), respectively. All the Fe₃O₄ nanoparticles were synthesized by the co-precipitation method. These particle sizes were chosen so that they are easily obtained for future commercial use. Commercial plastic products were obtained from two sources. A PET water bottle, a PS spoon, and a PP holder were acquired from a local supermarket, while LDPE, HDPE, and PVC containers came from MonotaRO Co., Ltd. (Osaka, Japan). All plastics were cut into smaller pieces and immersed in ethanol for 24 h to remove organic matter. The pieces were then dried at room temperature for 24 h, ground using an electric grinder and sieved to obtain MP particles ranging from 0.1 mm to 1 mm in size, which corresponds to the sizes of MPs generally contained in water [46,47]. Hydrochloric acid (HCl), and sodium hydroxide (NaOH), to examine the effect of pH on MPs adsorption, were purchased from FUJIFILM Wako Pure Chemical Corporation (Osaka, Japan).

The morphology of Fe₃O₄ nanoparticles and MPs before and after adsorption was observed by transmission electron microscopy (TEM, JEOL JEM-2010F, Akishima, Japan) and scanning electron microscopy (SEM, JEOL JEM IT-100, Akishima, Japan). ImageJ software (v1.52) was employed to process the TEM and SEM images. The crystallographic structure of Fe₃O₄ nanoparticles was characterized by X-ray diffraction (XRD, Rigaku SmartLab, Tokyo, Japan). A vibrating sample magnetometer (VSM, LakeShore 7404, Westerville, OH, USA) was used to determine the magnetic properties of Fe₃O₄ nanoparticles. Raman spectra were collected using a spectrophotometer (XPlora PLUS, Kyoto, Japan) to identify the composition of the plastics. Zeta potentials of Fe₃O₄ nanoparticles and MPs were measured by a dynamic light scatterometer (Zetasizer Nano ZS, Worcestershire, UK).

2.2. Adsorption Experiment of MPs by Fe₃O₄ Nanoparticles

Typically, 5 mg of Fe₃O₄ nanoparticles (15 nm, 100 nm and 300 nm) were mixed with 50 mL of MPs solution of a range of concentrations (10, 20, 30, 40, 50, 60, 70, and 80 g/L). The concentrations were selected to determine the adsorption capacity, and therefore, they are much higher than those in environmental water [48]. After calibrating the rotation speed and the duration of mixing, the vials were fixed on a shaker (310 rpm, 25 ± 1 °C) and run for 20 h. Fe₃O₄ nanoparticles were absorbed into the MPs, and the magnetic MPs were separated from the unmagnetic MPs at different time points. Subsequently, a magnet (250 N) was used to separate the aggregates of Fe₃O₄ nanoparticles and MPs from water. The aggregate was dried at room temperature for 24 h, weighed and the removal ratio was calculated using Equation (1).

$$\mathrm{R}\mathrm{e}\mathrm{m}\mathrm{o}\mathrm{v}\mathrm{a}\mathrm{l} \mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o} \left(\%\right)={\displaystyle \frac{C_0-C_e}{C_0}}\times 100$$

(1)

where $C_0$ is the initial MPs concentration (g/L), and $C_e$ represents the equilibrium MPs concentration (g/L).

All adsorption experiments were performed triplicate, and data were expressed as the mean ± standard deviation. The analysis of experimental data, including model fitting and the generation of graphical plots, was carried out using Origin 2025 and GraphPad Prism 9.0. The coefficient of determination (R²) was used to assess the goodness-of-fit for the applied adsorption isotherm and kinetic models. To identify statistically significant differences in the removal efficiency of Fe₃O₄ nanoparticles with varying particle sizes, one-way analysis of variance (ANOVA) was conducted, followed by Tukey’s post hoc test for multiple comparisons. A probability value (p) less than 0.05 was considered to indicate statistical significance.

2.3. Adsorption Model Analysis

The adsorption data were fitted using two adsorption isotherm models, the Langmuir model and the Freundlich model (Equations (2) and (3), respectively). The Langmuir model is used to describe the monolayer coverage process on a uniform structure of sorbent [49], and the Freundlich model is more applicable to multilayer adsorption processes and assumes that the adsorption surface is heterogeneous [50].

$$Q_e={\displaystyle \frac{Q_{max}{K}_L{C}_e}{1+{ K}_L{C}_e}}$$

(2)

$$Q_e=K_F{C}_e^{{\displaystyle \frac{1}{n}}}$$

(3)

where $Q_e$ (g/g) is the amount of MPs per unit mass of Fe₃O₄ particles at equilibrium, $Q_{max}$ (g/g) is the maximum adsorption capacity, $C_e$ (g/L) is the equilibrium concentration of MPs in solution, ${ K}_L$ (L/g) and $K_F$ (gg⁻¹(g/L)^−1/n) are the equilibrium constants of Langmuir model and Freundlich model, respectively. n is the Freundlich exponent.

The Pseudo-first-order model, Pseudo-second-order model and intra-particle diffusion model [51] were applied to fit the adsorption kinetic curves of Fe₃O₄ nanoparticles with three particle sizes, respectively.

$$q_t=q_e \left(1-e^{-K_{1}t}\right)$$

(4)

$$q_t={\displaystyle \frac{q_{e }^2{K}_{2}t}{1+q_e{K}_{2}t}}$$

(5)

$$q_t=K_p{t}^{0.5}+C$$

(6)

where $q_t$ (g/g) and $q_e$ (g/g) are the amounts of the MPs adsorbed at any time t (min) and equilibrium, respectively. $K_1$ (min⁻¹) and $K_2$ (g/(g·min)) are the pseudo first-order and pseudo second-order rate constants, respectively. $K_p$ (g/(g·min^1/2)) is the intra-particle diffusion rate constant. C (g/g) is the intercept of the intra-particle diffusion model.

3. Results and Discussion

3.1. Characterization of Fe₃O₄ Nanoparticles and MPs

As presented in Figure 1, Fe₃O₄ nanoparticles with nominal median particle sizes of 15 nm (Figure 1a), 100 nm (Figure 1b), and 300 nm (Figure 1c) all exhibited an approximately spherical morphology. Due to the high surface energy of the unmodified Fe₃O₄ nanoparticles, a certain degree of agglomeration was observed in all samples [52]. Size distribution of Fe₃O₄ nanoparticles was demonstrated by particle size analysis (Figure 2).

The XRD patterns for all Fe₃O₄ nanoparticles consistently revealed a pure spinel ferrite crystal structure (Figure 3). Distinct diffraction peaks were observed at 2θ values of approximately 30.1°, 35.4°, 43.1°, 53.4°, 56.9°, and 62.5°. These peaks correspond to the (220), (311), (400), (422), (511), and (440) crystal planes, which are characteristic of standard Fe₃O₄ (JCPDS Card No. 82-1533) [53]. The absence of any other significant diffraction peaks indicated that the crystal quality of all Fe₃O₄ nanoparticles was satisfactory.

Figure 4 shows that all three sizes of nanoparticles exhibited typical superparamagnetic behavior at room temperature. This is evidenced by the S-shaped hysteresis loops with near-zero coercivity and remanence. All samples reached saturation magnetization (Ms) under an applied magnetic field of 10 kOe. It is noteworthy that the larger-sized Fe₃O₄ nanoparticles exhibited a slightly higher Ms value, which is attributed to the lower percentage of magnetic disorder on the surface of the larger particles and a more complete internal spin arrangement [54].

MPs samples were identified by Raman spectroscopy, and the polymer composition was confirmed by comparison with standard spectral libraries (Figure 5). Specifically, PET was identified by its aromatic C=C ring vibration at ~1615 cm⁻¹ and characteristic C=O stretching at ~1730 cm⁻¹. PS showed distinctive aromatic C=C stretching peaks at ~1000 and ~1600 cm⁻¹, with a C-H stretching peak at ~3050 cm⁻¹. PP exhibited a strong C-H stretching peak at ~2900 cm⁻¹. HDPE and LDPE demonstrated similar characteristic peaks, but there were significant differences in peak intensity. The intensity of the C-C stretching vibration peaks (~1060 cm⁻¹ and ~1130 cm⁻¹) and the C-H stretching peak (~2850 cm⁻¹) is higher in HDPE. Similar method has been previously employed to distinguish between HDPE and LDPE [55]. The PVC spectrum displayed characteristic C-Cl stretching peaks at ~635 and ~695 cm⁻¹ and C-H stretching peak at ~2900 cm⁻¹.

The morphology of MPs samples was observed in SEM images (Figure 6). In general, all the MP samples were in the form of irregular fragments. Particle size analysis revealed comparable sizes of different MPs (Figure 7). HDPE showed the largest average size of 650 μm, followed by PET (631 μm), LDPE (607 μm), PVC (598 μm), PS (454 μm) and PP (447 μm).

SEM images of MPs (PET) after adsorption were shown in Figure 8. It can be observed that a large amount of Fe₃O₄ nanoparticles attached to the surface of MPs, forming a coating layer-like structure. This confirmed the successful capture of MPs by Fe₃O₄ nanoparticles, which led to the acquisition of magnetically responsive properties of MPs, and therefore can be easily separated from water using a magnet (Figure 9).

3.2. MPs Adsorption Experiments

The adsorption removal efficiencies of different sized Fe₃O₄ nanoparticles on six types of MPs were presented in Figure 10. The results suggested that all Fe₃O₄ nanoparticles could achieve over 60% removal efficiency for MPs at most concentration levels. The adsorption experiment results are consistent with observations from SEM (Figure 8) and optical characterization (Figure 9). For most MPs concentration levels, the removal efficiencies displayed a relatively clear and consistent size-dependent trend: 300 nm > 15 nm > 100 nm. This result indicated that particle size plays a critical and nonmonotonic role in determining the adsorption effectiveness. This non-linear relationship suggested that factors other than specific surface area are significantly influencing the adsorption process.

The removal efficiencies differ substantially among different types of MPs, especially at high initial concentrations. For example, at an initial concentration of 80 g/L, the removal efficiencies of 100 nm and 15 nm Fe₃O₄ nanoparticles for PET are much lower than those for other MPs. Previous research [54] has demonstrated that the adsorption efficiency of Fe₃O₄ nanoparticles for MPs is correlated with the hydrophobicity of MPs PET has the weakest hydrophobicity, making it less likely to be captured by Fe₃O₄ nanoparticles.

The initial concentration of MPs also strongly affected the removal performance of Fe₃O₄ nanoparticles. Typically, high MPs removal (>70%) could be achieved for all Fe₃O₄ nanoparticles at low concentrations (<30 g/L). Under these conditions, adsorption sites might be relatively abundant, resulting in less obvious performance differences among particles of varying sizes. However, as the initial concentration gradually increased and adsorption sites approached saturation, the adsorption was limited by their maximum adsorption capacities.

It can be clearly observed that MPs adsorbed with 100 nm Fe₃O₄ nanoparticles remained relatively light in color (Figure 11), suggesting a minimal amount of Fe₃O₄ particle attachment per unit mass of MPs. Conversely, MPs treated with 300 nm Fe₃O₄ nanoparticles exhibited a darker color as a result of adsorption of a greater number of magnetic particles. These visual observations are consistent with the microscopic evidence from SEM imaging and align well with adsorption performance data.

3.3. Adsorption Isotherms

The adsorption isotherm models and associated parameters are presented in Figure 12 and

Table 1. Adsorption isotherm parameters.

MPs	Fe3O4 (nm)	Langmuir			Freundlich
		${\mathit{K}}_{\mathit{L}}$ (L/g)	${\mathit{Q}}_{\mathit{m}\mathit{a}\mathit{x}}$ (g/g)	${\mathit{R}}^2$	${\mathit{K}}_{\mathit{F}}$ (gg−1(g/L)−1/n)	$\mathit{n}$	${\mathit{R}}^2$
PET	300	0.569	853.460	0.877	326.756	2.857	0.714
	100	–	–	–	–	–	–
	15	–	–	–	–	–	–
PS	300	0.284	1460.071	0.894	323.077	1.515	0.891
	100	0.043	1109.075	0.951	68.111	1.481	0.930
	15	0.120	1378.877	0.929	168.539	1.486	0.911
PP	300	0.422	1224.195	0.760	316.067	1.715	0.759
	100	0.274	519.388	0.950	152.876	2.857	0.948
	15	0.113	780.546	0.855	125.722	2.033	0.844
LDPE	300	0.196	1096.714	0.920	210.944	1.802	0.869
	100	0.052	886.367	0.929	76.290	1.585	0.904
	15	0.207	951.324	0.919	190.520	1.848	0.918
HDPE	300	0.123	1089.197	0.966	154.176	1.522	0.963
	100	0.210	537.237	0.939	140.871	2.703	0.921
	15	0.410	648.890	0.986	217.015	2.793	0.975
PVC	300	0.633	784.372	0.929	303.680	2.740	0.847
	100	0.132	647.696	0.950	119.869	2.203	0.940
	15	0.226	745.052	0.962	177.223	2.257	0.946

, and both models fit the adsorption data well. The values of the correlation coefficients R² fitted by the Langmuir model (0.760–0.986) were slightly higher than those of the Freundlich model (0.714–0.975), which implied that the adsorption of MPs by Fe₃O₄ is not an ideal monolayer coverage process [56]. The Langmuir model predicted that the theoretical maximum adsorption capacities ($Q_{max}$) for all MP types followed the size-dependent trend of 300 nm (784.372–1460.071 g/g) > 15 nm (648.890–1378.877 g/g) > 100 nm (519.388–1109.075 g/g), which is consistent with the experimental results. The 300 nm Fe₃O₄ nanoparticles have the highest adsorption capacity, thus maintaining their superior removal efficiency at high MPs concentrations (>50 g/L) (Figure 10). In contrast, the performance of the 100 nm Fe₃O₄ nanoparticles declined most markedly due to their lowest adsorption capacity. Furthermore, the value of adsorption intensity (0 < 1/n < 1) indicates that the adsorption is favorable [57].

Although the Langmuir model, which assumes monolayer adsorption on a homogeneous surface, generally provided slightly higher R². The reasonable fit of the Freundlich model that describes multilayer adsorption on a heterogeneous surface suggests a more complex mechanism. This is likely due to the heterogeneous nature of the MPs surfaces after mechanical grinding and the potential aggregation of nanoparticles, which resulted in the idealized assumptions of the Langmuir model not being fully satisfied.

PVC (647.696–784.372 g/g) and PET (853.460 g/g with 300 nm nanoparticles) showed lower adsorption capacities. This is because of the presence of polar C–Cl bonds in PVC and ester groups in PET, which enable a stronger surface polarity and hydrophilicity than other plastics [58,59]. Increased wettability leads to greater fluid resistance, resulting in decreased dispersibility and mobility in solution [60], thereby reducing collision frequency with Fe₃O₄ nanoparticles and consequently lowering adsorption efficiency.

It is worth noting that the adsorption capacities of all Fe₃O₄ nanoparticles used in this study are substantially higher than those of the hydrophobically modified Fe₃O₄ nanoparticles reported in previous studies [60,61,62]. This discrepancy may be attributed to the specific types of MPs and adsorbents employed. The MPs used in this study underwent pretreatment via mechanical grinding, a process that potentially simulates physical aging in natural environments, leading to the formation of oxygen-containing functional groups on their surfaces, and thus facilitating their adsorption by unmodified hydrophilic Fe₃O₄ nanoparticles [31].

3.4. Adsorption Kinetics

The adsorption kinetics curves were depicted in Figure 13. All MPs were quickly adsorbed within the first 10 min, the rapid removal of MPs by Fe₃O₄ nanoparticles has been previously reported [63]. Subsequently, the adsorption rate gradually decreased and reached the adsorption equilibrium within 30–60 min. Kinetic parameters listed in

Table 2. Adsorption kinetics parameters.

MPs	Fe3O4 (nm)	Pseudo-First-Order			Pseudo-Second-Order
		${\mathit{K}}_1$ (min−1)	${\mathit{q}}_{\mathit{e}}$ (gg−1)	${\mathit{R}}^2$	${\mathit{K}}_2$ (g(g·min)−1)	${\mathit{q}}_{\mathit{e}}$ (gg−1)	${\mathit{R}}^2$
PET	300	2.271	640.144	0.936	0.005	662.312	0.978
	100	0.922	304.001	0.892	0.005	313.481	0.925
	15	2.631	459.826	0.933	0.010	471.456	0.956
PS	300	2.467	708.695	0.900	0.004	736.272	0.950
	100	1.406	506.015	0.865	0.003	531.725	0.943
	15	2.229	652.827	0.906	0.004	679.310	0.958
PP	300	2.141	721.654	0.960	0.004	744.072	0.990
	100	0.503	433.282	0.859	0.002	450.147	0.941
	15	0.645	494.642	0.849	0.002	515.854	0.936
LDPE	300	3.001	667.169	0.938	0.007	685.172	0.966
	100	1.688	517.281	0.884	0.004	542.101	0.952
	15	2.231	604.105	0.926	0.005	626.179	0.970
HDPE	300	1.948	618.445	0.904	0.004	644.903	0.960
	100	1.597	450.065	0.856	0.004	474.185	0.933
	15	2.750	582.209	0.939	0.007	599.239	0.971
PVC	300	2.115	617.204	0.934	0.005	639.578	0.978
	100	1.280	483.548	0.866	0.003	507.887	0.945
	15	2.234	564.049	0.912	0.005	586.105	0.961

show that the pseudo-second-order model (R² = 0.925–0.990) described the data more accurately than the pseudo-first-order model (R² = 0.849–0.960), indicating that adsorption is dependent on both MPs concentration and the availability of binding sites on the adsorbents [64].

The intra-particle diffusion model was applied to investigate the decisive steps in the adsorption process [65]. As illustrated in Figure 14 and

Table 3. Intraparticle diffusion parameters.

MPs	Fe3O4 (nm)	${\mathit{K}}_{\mathit{p}1}$ (g/(g·min1/2))	${\mathit{C}}_1$	${\mathit{R}}_1^2$	${\mathit{K}}_{\mathit{p}2}$ (g/(g·min1/2))	${\mathit{C}}_2$	${\mathit{R}}_2^2$	${\mathit{K}}_{\mathit{p}3}$ (g/(g·min1/2))	${\mathit{C}}_3$	${\mathit{R}}_3^2$
PET	300	93.501	380.760	0.989	9.258	603.370	0.982	0.813	669.165	0.815
	100	61.200	122.125	0.979	6.347	304.435	0.999	−10.312	434.269	0.972
	15	56.530	304.129	0.996	5.510	473.122	0.999	−10.006	590.081	0.999
PS	300	61.842	470.493	0.997	9.785	690.604	0.999	0.556	763.073	0.983
	100	65.190	267.748	0.998	12.827	439.046	0.999	2.125	532.467	0.906
	15	70.093	407.856	0.997	9.300	623.660	0.999	1.088	694.841	0.965
PP	300	115.345	421.399	0.956	10.582	673.869	0.898	2.236	728.932	0.787
	100	64.618	170.124	0.999	15.251	337.168	0.999	3.898	414.026	0.768
	15	69.022	216.045	0.999	16.661	388.143	0.999	2.262	512.897	0.746
LDPE	300	52.656	486.419	0.999	17.386	573.043	0.999	0.921	707.922	0.894
	100	62.856	291.336	0.996	10.138	470.173	0.999	2.096	542.605	0.960
	15	67.698	380.538	0.953	13.732	542.098	0.986	1.470	628.841	0.783
HDPE	300	79.570	359.495	0.993	15.139	539.147	0.999	1.434	659.076	0.864
	100	50.539	252.042	0.998	10.534	400.100	0.999	2.274	473.364	0.972
	15	58.273	400.727	0.999	13.479	511.045	0.998	0.749	616.726	0.878
PVC	300	94.258	355.193	0.999	11.187	576.029	0.995	0.400	653.148	0.818
	100	64.802	247.078	0.998	12.694	427.971	0.999	1.367	509.566	0.840
	15	60.150	355.573	0.998	9.477	520.194	0.999	2.284	584.626	0.945

, the initial sharp linear stage lasted for 10–30 min and was attributed to the diffusion of Fe₃O₄ through the boundary layer from water to the surface of the MPs [66]. The second segment represents a slower adsorption driven by intra-particle diffusion, persisting until the binding sites at the Fe₃O₄/MPs interface approach saturation [67]. Finally, the adsorption process reaches the equilibrium stage. The mass transfer trend was similar for all Fe₃O₄ nanoparticles, exhibiting a fast-then-slow pattern. This mass transfer mechanism is consistent with literature reports [68,69,70], where external diffusion dominates the early stage, after which intra-particle diffusion becomes the rate-controlling step. The non-zero and progressively larger intercepts (C values) in each segment signify an increasing boundary-layer thickness, indicating that intra-particle diffusion is not the only rate-limiting step [71], but is extremely important in the adsorption process.

3.5. Effect of pH on MPs Adsorption

As demonstrated in Figure 15, the solution pH significantly influenced the removal efficiency. The 15 nm and 300 nm Fe₃O₄ nanoparticles achieved the highest removal efficiencies at pH 6.5, reaching 63.6–88.7% and 82.8–95.8%, respectively. The 100 nm particles exhibited maximum removal for PS, PP, and PVC at pH 4.5. All Fe₃O₄ nanoparticles showed a marked decrease in removal efficiency at both low and high pH values. It can be seen from Figure 16 that the isoelectric points (IEPs) of the 15 nm, 100 nm, and 300 nm Fe₃O₄ nanoparticles are approximately 7.0, 5.0, and 7.9, respectively. In contrast, all six types of MPs were negatively charged across the tested pH range. At pH 6.5, the positively charged 15 nm and 300 nm Fe₃O₄ nanoparticles were easily adsorbed onto the negatively charged MPs surface due to strong electrostatic attraction, resulting in an efficient MPs removal. The 300 nm Fe₃O₄ nanoparticles exhibited a higher absolute zeta potential value at this pH, leading to stronger electrostatic attraction and thus a higher removal rate. When pH rises to 8.5, the medium exceeds the IEPs of the 15 nm and 300 nm particles, rendering the surfaces negatively charged and generating electrostatic repulsion with the MPs, thereby causing a drastic decrease in the adsorption efficiency. The 100 nm particles become negatively charged at pH > 5.0, repelling the MPs and resulting in significantly lower removal rates at near-neutral pH (6–7) compared to the other sizes. The lower removal rates observed at the pH extremes can be contributed to intensified electrostatic repulsion. These findings confirm that electrostatic interaction is the key mechanism regulating the adsorption of MPs by Fe₃O₄ nanoparticles. In practical applications, the removal efficiency of Fe₃O₄ nanoparticles is expected to be higher in nearshore marine environments (pH ∼ 7) than in open oceans (pH = 8–8.5), as the pH is closer to the optimal conditions. Consequently, Fe₃O₄ nanoparticles have greater application potential for MPs remediation in coastal waters, particularly in nearshore environments with relatively lower pH, such as estuaries and harbors.

3.6. Mechanism of Size-Dependent Adsorption of MPs

According to the results of removal experiments and characterization analysis, we proposed the mechanism of size-dependent adsorption of MPs by Fe₃O₄ nanoparticles. The observed non-monotonic adsorption trend (300 nm > 15 nm > 100 nm) strongly indicates that the adsorption process is not dominated by the specific surface area, as smaller particle size does not necessarily result in superior adsorption performance. On the contrary, electrostatic attraction plays a crucial role in adsorption. This is substantiated by the strong correlation between the removal efficiency trends at different pH values and the zeta potential curves of Fe₃O₄ nanoparticles. The removal efficiency peaks under pH conditions that maximize the electrostatic attraction between the positively charged Fe₃O₄ nanoparticles and the negatively charged MPs surfaces. A maximized electrostatic repulsion arises from equally charged Fe₃O₄ nanoparticles and MPs surfaces, leading to the lowest removal efficiency.

The key to the size-dependent adsorption of MPs is that Fe₃O₄ nanoparticles of different sizes exhibit distinct surface charge properties, which are manifested as their varying IEPs. The IEP of nanoparticles may be related to their size [72]. It has been reported that the IEP of hematite nanoparticles shifts to lower pH values as their particle size decreases, likely due to alterations in electronic structure and surface charge distribution [73]. Furthermore, the synthesis routes and experimental procedures can also influence the IEP of nanoparticles [74]. Although all the Fe₃O₄ nanoparticles used in this study were synthesized via the co-precipitation method, differences in the synthesis conditions resulted in size variations [75], which can also explain the disparities in IEPs. These differences in surface electrochemistry originate from the influence of synthesis conditions on the surface physical properties of the nanoparticles. Variations in synthesis parameters can substantially affect the distribution and nature of surface hydroxyl groups on Fe₃O₄ nanoparticles. Disparities in the surface concentration of acidic and basic hydroxyl groups lead to differences in IEPs [76]. For instance, a higher surface concentration of basic hydroxyl groups results in a higher IEP. The protonation and deprotonation behavior of these groups is the primary factor that determines the surface charge and IEP of metal oxides [77]. Hence, the non-monotonic adsorption capability results from variations in the surface electrochemistry and charge of Fe₃O₄ nanoparticles of different sizes and ultimately determining the electrostatic interaction between Fe₃O₄ nanoparticles and MPs.

The properties of microplastics also influence adsorption performance. Although electrostatic attraction is the primary driving force for adsorption, some hydrophilic MPs with polar groups (such as PVC and PET) exhibit reduced mobility in aqueous phases. This decrease in mobility lowers their collision frequency with Fe₃O₄ nanoparticles, thereby modulating the overall adsorption capacity.

Moreover, the MPs used in this study are considerably larger in size than the Fe₃O₄ nanoparticles, allowing the nanoparticles to adhere to the MPs surfaces and form a coating layer. Prior research [78] has shown that the coverage efficiency is low when nanoparticles are of a similar size to the substrate particles. Conversely, smaller size ratios result in significantly improved coverage efficiency. Therefore, smaller-sized Fe₃O₄ nanoparticles theoretically enable higher coverage. Nevertheless, the results suggest that larger nanoparticles achieved more effective attachment. In addition to electrostatic interactions, this may also result from enhanced magnetic responsiveness. The magnetic moment of magnetic nanoparticles increases with particle size [54], and the 300 nm Fe₃O₄ nanoparticles possess a higher magnetic moment, which can impart sufficient magnetic responsiveness to MPs, facilitating effective recycling under an external magnetic field [35].

On the other hand, while the high concentrations used in this study were necessary to determine the maximum adsorption capacities, the fundamental mechanism of electrostatic interaction is concentration-independent and is expected to govern the adsorption process at environmentally relevant concentrations as well. A high removal efficiency is anticipated under low-concentration conditions where sufficient adsorption sites are available.

4. Conclusions

This study systematically investigated the adsorption behavior of three sizes (15 nm, 100 nm, and 300 nm) of Fe₃O₄ nanoparticles towards common MPs, revealing a non-monotonic size-dependent adsorption trend. Based on adsorption isotherms and kinetic results, the largest nanoparticles exhibited the best adsorption efficiency and capacity for all MP types, followed by the small-sized, and then the medium-sized. The adsorption data were well-fitted by the Langmuir isotherm and pseudo-second-order kinetic models. Analysis of varying pH and zeta potentials indicated that electrostatic interaction is the dominant adsorption mechanism. At near-neutral pH conditions, the 300 nm Fe₃O₄ nanoparticles carried a greater net positive surface charge, resulting in the strongest electrostatic attraction and thus the highest removal efficiency. Conversely, the 100 nm Fe₃O₄ nanoparticles were negatively charged under near-neutral pH conditions, leading to electrostatic repulsion and inhibiting MP adsorption. These findings deepen the understanding of how the size of nanoparticles affects MP adsorption and provide important information for designing optimized magnetic nano-adsorbents for sustainable water treatment.