Adsorption Performance of Fe₂O₃-Modified Dolomite Composite (DFC) for Congo Red Removal

Abstract

Printing and dyeing wastewater is known for its high color intensity, complex composition, and low biodegradability, making its treatment a significant challenge in environmental protection. Dolomite is a natural mineral with abundant reserves and can be effectively used as an adsorbent carrier. In this study, the dolomite loaded by Fe₂O₃ composites (DFC) was synthesized and systematically characterized using XRD, SEM, TEM, BET, XPS, and IR to evaluate its structural and surface properties. The adsorption performance of DFC on Congo Red (CR) was then investigated. The maximum adsorption amount of CR by DFC was 3790.06 mg⋅g⁻¹, and the removal rate was still stable at 97% after five cycles of adsorption test, which demonstrated that DFC exhibited exceptional adsorption efficacy and regeneration capability. The loaded Fe³⁺ was beneficial to improve the adsorption effect on the DFC. In addition, to evaluate the type of adsorption, kinetic calculations were performed, which indicated that the Weber–Morris diffusion modeling study showed the adsorption behavior was influenced by the interplay of many diffusion mechanisms. The study offers an innovative method for the efficient utilization of dolomite in creating renewable adsorbent materials for dye wastewater remediation.

1. Introduction

The swift proliferation of industrialization and urban development has resulted in the release of substantial quantities of industrial wastewater, which harbors detrimental organic pollutants that pose serious risks to ecosystems and well-being for humans [1,2,3]. CR dye wastewater has drawn significant attention in water pollution control research because of its stable chemical structure, low biodegradability, environmental persistence, and high toxicity [4,5].

Wastewater treatment is a crucial technical field for addressing water resource pollution. Commonly used methods include chemical oxidation [6,7], flocculation precipitation [8,9], biodegradation [10,11], ion exchange [12,13], and physical adsorption [14,15]. Among these, physical adsorption is a primary method for treating wastewater containing toxic organic pollutants owing to its straightforward approach, wide applicability, and effectiveness [16]. The efficiency of physical adsorption is largely influenced by the properties and performance of the materials used for adsorption, making their selection and optimization critical to improving adsorption efficiency. Common adsorbent materials for wastewater treatment include porous materials like activated carbon [17], zeolite [18], diatomite [19], and metal–organic frameworks (MOFs) [20]. These materials typically possess large specific surface areas, rich pore structures, and excellent adsorption capacities, making them highly effective in removing organic dyes and other pollutants. Smedt et al. prepared activated carbon using the eutectic mixture activation technique, exhibiting an adsorption limit of 447 and 146 mg·g⁻¹ for methyl orange and for Brilliant Blue FCF, respectively [21]. Ritter et al. synthesized Linde A-type (LTA) zeolite from hazardous aluminum waste, achieving a 98.12% removal efficiency for textile dyes when used as an adsorbent [22]. Chen et al. reported that sodium humate-modified diatomaceous earth exhibited a capacity for adsorption of 75.1 mg·L⁻¹ for tetracycline wastewater, achieving a tetracycline removal efficiency of 78.3% [23]. Liu et al. synthesized a composite material based on MOFs that achieved removal rates exceeding 99% for methyl orange, methyl yellow, and malachite green [24]. Ren et al. synthesized Co-MOF, which achieved a maximum adsorption capacity of 355.2 mg⋅g⁻¹ for CR within 5 h [25]. Liu et al. reported that FexCo₃-xO₄ nanoparticles exhibited a maximum adsorption capacity of 128.6 mg⋅g⁻¹ for CR at equilibrium [26].

Dolomite (CaMg(CO₃)₂), a natural mineral, shows promising adsorption applications due to its surface adsorption capabilities, pore channel filtration effects, and ion exchange between mineral layers [27,28]. However, the natural adsorption capacity of dolomite is relatively low, making it insufficient for practical wastewater treatment needs. To improve its adsorption performance, modification methods such as metal oxide loading are commonly used to enhance its pollutant removal efficiency. Modified adsorbent materials containing Fe₂O₃ have proven highly effective in eliminating toxic metal ions and organic contaminants. Pham et al. demonstrated that Fe₂O₃-modified diatomaceous earth achieved an approximately 90.03% removal efficiency for CIP and retained an over 80% efficiency after five reuse cycles [29]. Li et al. developed an unusual adsorbent (HFO-BC), by modifying biochar (BC) with aqueous ferric oxide (HFO). Experimental results showed maximum adsorption capacities of 29 and 34 m·g⁻¹ for Cd²⁺ and Cu²⁺, respectively [30]. Oyehan et al. demonstrated that Fe₂O₃-modified carbon nanofibers (CNF) achieved an approximately 80% removal efficiency for Cd(II) within 30 min [31]. These studies clearly demonstrate the effectiveness of Fe₂O₃ in improving adsorbent performance. However, studies exploring the combination of Fe₂O₃ with dolomite for property enhancement are relatively rare. Developing efficient composite adsorbent materials by leveraging the natural porous structure of dolomite and the superior adsorption capacity of Fe₂O₃ is crucial.

This study investigated a highly effective, affordable, and environmentally friendly adsorbent (DFC) for treating anionic dye wastewater. The DFC was systematically identified by XRD, BET, SEM, and TEM, and the influence of adsorption time, temperature, and pH on its performance were analyzed. Additionally, the mechanisms of adsorption, kinetics, and diffusion were examined in detail to improve understanding of its functional properties and applications.

2. Experimental

2.1. Materials

Dolomite in powder form (CaMg(CO₃)₂, 184.41 g⋅mol⁻¹, Bao Feng Mining Co. (Laibin, China)). Ferric oxide in powder form (Fe₂O₃, 159.69 g⋅mol⁻¹, 99.5%, Shandong Keyuan Biochemical Co. (Shandong, China)). CR in powder form (C₃₂H₂₂N₆Na₂O₆S₂, 696.66 g⋅mol⁻¹, Indicator grad, Shanghai Macklin Biochemical Co., Ltd. (Shanghai, China)). Sodium hydroxide in flake form (NaOH, 40 g⋅mol⁻¹, 96%, Shanghai Macklin Biochemical Co., Ltd. (Shanghai, China)). Hydrochloric acid in liquid form (HCl, 34.46 g⋅mol⁻¹, 36–38%, Sichuan Xilong (Chengdu, China)). All water used during the experimental procedures was deionized.

2.2. Preparation of DFC

Dolomite (CaMg(CO₃)₂) and α-Fe₂O₃ were put into the ethanol solution at a mass ratio of 100:1, thoroughly mixed by stirring, and subsequently submitted to ball milling for 2 h at 200 rpm. After ball milling, the samples were subjected to centrifugation to eliminate ethanol and were dried afterward at 60 °C. Next, the obtained samples were heated in muffle furnace at 500 °C for 4 h to obtain DFC [29].

2.3. Characterization of DFC

FTIR (Thermo Fisher Scientific Nicolet iN10) was utilized to examine the functional groups; SEM (TESCAN MIRA LMS) was employed to analyze the microscopic morphology, with elemental distributions evaluated using energy-dispersive spectroscopy (EDS, TESCAN MIRA LMS, TESCAN, Brno, Czech Republic); specific surface area and pore size distribution were measured using BET (Micromeritics ASAP 2460, Micromeritics ASAP 2460, Micromeritics, Norcross, GA, USA)analysis; crystal structure was assessed by XRD (Rigaku SmartLab SE, Rigaku, Tokyo, Japan); and the surface elemental composition and chemical states were examined by XPS (Thermo Scientific K-Alpha, Thermo Fisher Scientific, Waltham, MA, USA).

2.4. Recycling and Regeneration of DFC

After the adsorption process was completed, the spent DFC was separated from the solution by filtration or centrifugation. To regenerate the adsorbent, the collected material was subjected to calcination in a muffle furnace at 300 °C for 1 h, effectively removing the adsorbed CR. The regenerated DFC was then collected and used in subsequent adsorption cycles to evaluate its reusability and cycling stability.

2.5. Adsorption Capability Evaluation

An amount of 50 mg of DFC was combined with 100 mL of CR solutions at varying concentrations. The selection of this adsorbent-to-solution ratio was based on preliminary adsorption studies, ensuring an optimal balance between sufficient adsorption capacity and measurable concentration changes in the solution. This ratio aligns with previous studies on similar adsorbents, facilitating comparability with existing literature. Moreover, this proportion ensures that the adsorption sites are not excessively saturated or underutilized, thereby providing reliable kinetic and equilibrium data. The mixture was stirred at 500 rpm within a thermostatically controlled water bath, with the temperature maintained between 298 K and 318 K, at pH = 5, and the samples were collected at various time intervals. The level of CR in the solution was measured by spectrophotometer equipped with an UV–Vis and the equilibrium capacity of adsorption (Q_e, mg⋅g⁻¹) and rate of removal (R, %) were subsequently estimated [32]. The equations are delineated in Equations (1) and (2), in which C₀ and C_e denote the initial and equilibrium concentrations (mg⋅L⁻¹) of the CR solution, respectively; V signifies the volume of the solution (L); and m indicates the mass of the DFC (g). To ensure reproducibility, each experiment was performed three times under consistent conditions.

$${\mathrm{Q}}_{\mathrm{e}}={\displaystyle \frac{\left({\mathrm{C}}_0{-\mathrm{C}}_{\mathrm{e}}\right)\times \mathrm{V}}{\mathrm{m}}}$$

(1)

$$\mathrm{R}\left(\%\right)={\displaystyle \frac{\left({\mathrm{C}}_0-{\mathrm{C}}_{\mathrm{e}}\right)\times 100}{{\mathrm{C}}_0}}$$

(2)

3. Results and Discussion

3.1. DFC Characterization

The structure, morphology, and chemical content of the DFC were analyzed utilizing XRF, XRD, IR, XPS, SEM, and TEM techniques.

3.1.1. XRF Analysis

Based on the XRF analysis results shown in

Table 1. Elemental composition of dolomite (CaMg(CO₃)₂) obtained by X-ray fluorescence (XRF).

No.	Element	Result	Unit
1	O	55.871	Mass%
2	Ca	31.082	Mass%
3	Mg	12.784	Mass%
4	other	0.263	Mass%

, the mass percentages of magnesium and calcium are 12.78% and 31.08%, respectively. This indicates that the sample is primarily composed of magnesium carbonate and calcium carbonate, which aligns with the chemical composition characteristics of dolomite.

3.1.2. XRD Analysis

Figure 1 illustrates the XRD images of DFC composites. The reflection peaks of dolomite occur at 2θ values of 30.78°, 41.08°, and 50.58°, relating to the (104), (110), and (113) crystal planes of CaMg(CO₃)₂, respectively [33]. The distinctive diffraction peaks of α-Fe₂O₃ were absent in the XRD patterns of DFC composites. This absence could be due to the low content of α-Fe₂O₃, with its signals potentially obscured by the stronger diffraction peaks of dolomite, making detection challenging [34].

3.1.3. FTIR Analysis

The FTIR analysis in Figure 2a shows that for pure dolomite, the peak at 3438.72 cm⁻¹ correspond to the stretching vibration of surface hydroxyl groups [35]. The peaks at 2528.13 cm⁻¹ and 1436.37 cm⁻¹ represented the resonance vibration of C=O and the asymmetric stretching vibration of CO₃²⁻ in carbonates, respectively. Additionally, the peaks at 881.61 cm⁻¹ and 724.71 cm⁻¹ were attributed to in-plane bending and the latter to out-of-plane bending [36,37]. The modified DFC composites demonstrated a decrease in intensity and a displacement in the position of the 724.71 cm⁻¹ peak, indicating that the incorporation of α-Fe₂O₃ induced distortions or defects in the dolomite lattice, thereby affecting the vibrational mode of CO₃⁻ [38]. Additionally, the enhancement of the 580.25 cm⁻¹ peak belonged to the Fe-O vibration of α-Fe₂O₃ [39], confirming the successful loading of α-Fe₂O₃ onto the composite.

3.1.4. XPS Analysis

Figure 2b displays the XPS of DFC. The Fe 2p characteristic peaks were deconvoluted into Fe 2p_3/2 at about 710 eV and Fe 2p_1/2 at around 724 eV [40], indicating that α-Fe₂O₃ existed in Fe³⁺. Furthermore, no Fe²⁺ signal was detected, suggesting that no considerable redox reaction occurred during the loading process. The presence of Fe³⁺ introduced potential active sites, while the stable chemical state of α-Fe₂O₃ after loading ensured the modified materials maintain excellent structural stability and functional properties. These properties offered crucial theoretical support for applications in catalysis, adsorption, and environmental treatment [41].

3.1.5. SEM and TEM Analysis

SEM analysis (Figure 3a) indicates that the dolomite surface exhibited a smooth texture with characteristic layered crystalline features. In contrast, the modified DFC composite (Figure 3b) exhibited a significantly rougher surface, primarily due to the deposition of α-Fe₂O₃. TEM (Figure 3c) and elemental distribution maps (Figure 3d–f) confirmed the uniform distribution of Fe elements, indicating that α-Fe₂O₃ was well-dispersed in the composite without significant agglomeration. This homogeneous dispersion was pivotal to improving the material’s performance.

3.2. Surface and Thermal Properties

As illustrated in Figure 4a,b and

Table 2. Specific surface area, total pore volume, and average pore size of the dolomite and DFC.

Sample	Specific Surface Area (m2⋅g−1)	Total Pore Volume (cm3⋅g−1)	Average Pore Diameter (nm)
Dolomite	1.47	0.006	16.61
DFC	4.89	0.040	32.66

, the modification with α-Fe₂O₃ significantly enhanced the specific surface area of the surface of dolomite from 1.47 m²⋅g⁻¹ to 4.89 m²⋅g⁻¹, increased the total pore volume from 0.006 cm³⋅g⁻¹ to 0.040 cm³⋅g⁻¹, and enlarged the average pore size from 16.61 nm to 32.66 nm. This indicated that the modification effectively improved the material’s pore structure and surface properties. The adsorption–desorption isotherms revealed a significant increase in nitrogen adsorption for the DFC composite in the elevated relative pressure region (P/P₀ > 0.8). This reflected a higher proportion of mesopores, enhanced diffusion performance, and improved macromolecular adsorption of reactants. The improvements were mainly related to the remodeling of pore structures and the introduction of more active sites through interfacial interactions and lattice distortions after α-Fe₂O₃ modification [42].

Figure 4c shows that during thermogravimetric analysis, dolomite decomposed between 621.75 °C and 680.81 °C, resulting in a weight loss of 47.48%, primarily due to the formation of oxides (CaO and MgO) and the release of CO₂ [33]. After α-Fe₂O₃ loading, the decomposition start temperature was reduced to 607.59 °C., the termination temperature dropped to 667.93 °C, and the weight loss percentage was reduced to 40.95%. This indicated that α-Fe₂O₃ catalyzed carbonate decomposition and accelerated CO₂ release by providing activation sites or altering the reaction pathway [43]. Additionally, the decomposed residue, potentially containing CaO, MgO, and α-Fe₂O₃, offered opportunities for high-temperature catalysis or adsorption applications.

3.3. Adsorption Properties

3.3.1. Temporal Variation in CR Removal by DFC

The removal of CR over DFC was employed (Figure 5a). The removal rate gradually increased with the time gone by, achieving 99% after 120 min. The change in the solution’s hue verified this phenomenon. The initially deep red CR solution gradually became colorless upon adsorption, confirming that DFC efficiently adsorbs.

3.3.2. Structural Characteristics of CR

CR is an unusual amino dye, containing a chemical structure with an -N=N- azo group and a -SO₃Na group, which provide significant water solubility and negative charge properties (Figure 5c).

3.3.3. Effect of pH on CR Adsorption and Zeta Potential of DFC

Figure 5b illustrates the influence of pH for the removal of CR. The adsorption of CR declined with the pH increased. At higher pH, the surface of DFC obtained a negative charge, which led to electrostatic repulsion and a decrease in adsorption. Conversely, at a low pH level, the positively charged surface of DFC attracts negatively charged CR molecules, causing a substantial increase in adsorption. Based on these observations, pH = 5 was selected as the optimal condition for subsequent adsorption experiments. This is because, at pH = 5, DFC maintains a sufficiently positive surface charge, which favors electrostatic attraction with the anionic CR. Moreover, this pH value is representative of many real wastewater conditions and helps avoid possible interference from the hydrolysis or precipitation of Fe-based components.

3.3.4. Effect of Temperature on CR Adsorption

Figure 5d shows that the removal rate escalated from 47% to 98% due to the temperature increased from 298 K to 318 K, showing that the DFC exhibited an increase in enthalpy. The rise in temperature improved the thermal mobility of CR molecules, increasing their diffusion to the surface of DFC.

Table 3. The comparison for CR removal over different adsorbents.

Author	Material	Time (h)	Qe (mg/g)	Temperature (K)	Ref.
Q. Ren	Co-MOF1	0.6	1100	298	[25]
J. Liu	FexCo3−xO4	4	128.6	298	[26]
P.F. Silva	A-1/1	2.5	652.5	303	[44]
O. Khan	2D SAPO	6	26.6	298	[45]
A. Hamd	GLACT2M	6	11.9	298	[46]
S. Qiu	CAM	2.5	334.92	298	[47]
G.M. Galvani	NiO300	2	259.74	308	[48]
S. Zhai	N-MCA	0.8	431	318	[49]
S. Wang	PCMCA-900	35	652.3	308	[50]
F. Aghaei	EC/ZIF-67	0.8	357.42	298	[51]
Pengfei yang	DFC	3	3790.06	318	This work

summarizes equilibrium adsorption capacities of various adsorbents for CR solutions, including MOFs, carbon-based materials, and composites. The DFC synthesized in this study demonstrated exceptional performance with an adsorption capacity of 3790.6 mg⋅g⁻¹, underscoring its efficiency and potential for wastewater treatment applications.

3.4. Analysis of Adsorption Isotherms

To gain deeper insight into the interaction between CR and DFC, the isotherms of adsorption were evaluated by altering the starting solution concentration, as illustrated in Figure 6a. The results were further evaluated and fitted utilizing the Langmuir [52], Freundlich [53], and Dubinin–Radushkevich (D-R) models [54], with the findings presented in

Table 4. The calculated parameters of the adsorption isotherm.

Model	Parameters	Values
Langmuir	qm	3790.06 mg⋅g−1
	KL	0.03274 L⋅mg−1
	R2	0.996
Freundlich	KF	378.6 mg⋅g−1
	n	202.6
	R2	0.901
D-R	Qm	3265.7 mg⋅g−1
	E	73.4 KJ⋅mol−1
	R2	0.973

and Figure 6b–d.

3.4.1. Langmuir Isotherm Model

The Langmuir isotherm models adsorption on a uniform surface, where molecules form a monolayer, and each adsorption site is identical. It assumes a limited quantity of adsorption sites and the absence of interactions among the adsorbed molecules. The model can be articulated as Equation (3):

$${\mathrm{q}}_{\mathrm{e}}={\displaystyle \frac{{\mathrm{q}}_{\max }{\mathrm{K}}_{\mathrm{L}}{\mathrm{C}}_{\mathrm{e}}}{1+{\mathrm{K}}_{\mathrm{L}}{\mathrm{C}}_{\mathrm{e}}}}$$

(3)

where q_max represents the maximum monolayer adsorption capacity (mg⋅g⁻¹), K_L is the Langmuir constant (L⋅mg⁻¹), associated with the affinity of binding sites, and C_e presents the equilibrium concentration (mg⋅L⁻¹).

3.4.2. Freundlich Isotherm Model

The Freundlich isotherm characterizes adsorption on surfaces with heterogeneity, where multiple layers of adsorbate are formed and adsorption energies are not uniform across the surface. The equation is expressed as Equation (4):

$${\mathrm{Q}}_{\mathrm{e}}={\mathrm{K}}_{\mathrm{F}}{\mathrm{C}}_{\mathrm{e}}^{{\displaystyle \frac{1}{\mathrm{n}}}}$$

(4)

Taking the logarithmic form as Equation (5):

$$\ln {\mathrm{Q}}_{\mathrm{e}}=\ln {\mathrm{K}}_{\mathrm{F}}+{\displaystyle \frac{1}{\mathrm{n}}}\ln {\mathrm{C}}_{\mathrm{e}}$$

(5)

where ${\mathrm{K}}_{\mathrm{F}}$ presents the Freundlich of constant (mg⋅g⁻¹) and $\mathrm{n}$ presents the heterogeneity factor.

3.4.3. Dubinin–Radushkevich (D-R) Isotherm Model

The D-R isotherm is utilized to assess the porosity and adsorption mechanisms, allowing for differentiation between physisorption and chemisorption processes. The model is expressed as Equation (6):

$${\mathrm{Q}}_{\mathrm{e}}={\mathrm{Q}}_{\max }\exp \left(-{\mathrm{K}}_{\mathrm{D}}{\mathsf{\epsilon }}^2\right)$$

(6)

where Q_max is the theoretical saturation capacity (mg/g) and ${\mathrm{K}}_{\mathrm{D}}$ is a adsorption energy constant (mol²⋅kJ⁻²).

ε is the Polanyi potential, as Equation (7) shows:

$$\mathsf{\epsilon }=\mathrm{RTln}\left(1+{\displaystyle \frac{1}{{\mathrm{C}}_{\mathrm{e}}}}\right)$$

(7)

Here, R is 8.314 J⋅mol⁻¹·K⁻¹ and T is the temperature (K).

Equation (8) is the mean adsorption energy:

$$\mathrm{E}={\displaystyle \frac{1}{\sqrt{2{\mathrm{K}}_{\mathrm{D}}}}}$$

(8)

Figure 6a shows that DFC achieved a high level of CR adsorption of 3790.06 mg⋅g⁻¹ at 318 K, exhibiting higher efficacy. The fitting findings indicated that the correlation value of the Langmuir model (R² = 0.996) was considerably better than that of the Freundlich model. This showed that the DFC adsorption mainly belonged to a monomolecular layer mechanism, which has a more uniform distribution of adsorption sites. The E was 73.4 kJ/mol calculated from the D-R model, which was higher than 16 kJ⋅mol⁻¹. This confirms that the absorption reaction was primarily controlled by chemisorption [44], showing the existence of powerful contact forces within the system.

3.5. Mechanism Study of Adsorption

Reaction time serves as a critical metric for adsorption efficiency, directly affecting both the operational velocity for the adsorption process and its efficacy. The adsorption efficiency (Figure 7a) first increased significantly, then gradually decreased, before reaching equilibrium. The fast initial adsorption primarily results from the lamellar configuration of DFC, which offers several adsorption sites. To thoroughly investigate the adsorption mechanism of CR on DFC adsorbents, kinetic modeling combined with diffusion analysis was applied (Figure 7b–d). The pseudo-first-order and pseudo-second-order models were utilized to analyze the adsorption kinetics, while the Weber–Morris diffusion model was used to dissect the diffusion-controlled stages of the process. This multifaceted approach provided a deeper understanding of the underlying mechanisms governing the adsorption behavior. The pseudo-first-order model is described by Equation (9) [55]:

$$\ln \left({\mathrm{q}}_{\mathrm{e}}-{\mathrm{q}}_{\mathrm{t}}\right)=\ln {\mathrm{q}}_{\mathrm{e}}-{\mathrm{K}}_1\mathrm{t}$$

(9)

where ${\mathrm{q}}_{\mathrm{e}}$ and ${\mathrm{q}}_{\mathrm{t}}$ are the adsorption capabilities (mg⋅g⁻¹) at equilibrium and at time t, respectively, and ${\mathrm{K}}_1$ represents the pseudo-first-order rate constant (min⁻¹).

The pseudo-second-order model is calculated by Equation (10) [56]:

$${\displaystyle \frac{\mathrm{t}}{{\mathrm{q}}_{\mathrm{t}}}}={\displaystyle \frac{1}{{\mathrm{K}}_2{\mathrm{q}}_{\mathrm{e}}^2 }}+{\displaystyle \frac{\mathrm{t}}{{\mathrm{q}}_{\mathrm{e}}}}$$

(10)

where k₂ is the pseudo-second-order rate constant (gmg⁻¹⋅min⁻¹).

The Weber–Morris diffusion model can be expressed as shown in Equation (11) [57]:

$${\mathrm{q}}_{\mathrm{t}}={\mathrm{k}}_{\mathrm{id}}{\mathrm{t}}^{0.5}+\mathrm{C}$$

(11)

where k_id is the intraparticle diffusion rate constant (mg⋅g⁻¹⋅min^−0.5) and C represents the boundary layer effect.

The results show that the CR adsorption process is a dynamic, multistep process governed by various mechanisms, as illustrated in Figure 7 and

Table 5. The calculated kinetic parameters.

Model	Parameters	Values
Pseudo-first-order	qe,cal	142.17 mg⋅g−1
	K1	0.0197 min−1
	R2	0.838
Pseudo-second-order	qe,cal	420.17 mg⋅g−1
	K2	0.00018 g·mg−1·min−1
	R2	0.994
Weber–Morris	C1	−129.33
	K1	80.18 mg⋅g−1·min−0.5
	R2	0.976
	C2	365.519
	K2	1.17 mg⋅g−1·min−0.5
	R2	0.977

. The adsorption action is predominantly influenced by chemisorption, as evidenced by the better fit of the pseudo-second-order model (R² = 0.994) in comparison to the pseudo-first-order model [58]. The measured equilibrium adsorption capacity (q_e,cal = 420.17 mg⋅g⁻¹) roughly aligns with the results of the experiment. Further study of the Weber–Morris internal diffusion model shows that the rate constants K₁ > K₂ indicates a multi-stage diffusion control mechanism in the DFC adsorption process. External diffusion predominates in the first stage, and the intercept C₁ = −129.33 indicated that external diffusion is not the sole governing factor. The boundary layer effect greatly impacts the adsorption rate [59]. As time goes on, the DFC adsorption is increasingly controlled by internal diffusion, which requires the entry of solute molecules into the internal pores of the adsorbent. The diffusion rate is constrained by resistance and thus diminishes. Overall, the adsorption kinetics of CR are mainly determined by chemisorption and are influenced by a combination of multi-stage diffusion mechanisms and boundary layer effects.

3.6. Cycling Stability and Performance of the DFC

To assess the recycling efficiency of the DFC, its performance was evaluated over five consecutive adsorption–desorption cycles. Figure 8a illustrates that the adsorbent’s removal effectiveness exceeded 97% after five cycles, indicating minimal decline in performance. This suggests that the adsorbent maintains its adsorption capacity and effectiveness over prolonged use, demonstrating good regeneration and long-lasting performance. Similarly, Figure 8b shows the XRD results before and after adsorption shows that the adsorbent’s crystal structure remains stable, with no significant structural disruption or phase transformation. This further demonstrates that the adsorbent retains its original crystal structure, with no significant lattice mismatch or other undesirable changes, even after multiple adsorption and desorption cycles. These results demonstrate that the adsorbent exhibits excellent stability and efficient regeneration throughout the adsorption–desorption cycle. Its excellent cyclic stability and structural integrity make it an ideal material for long-term use.

4. Practical Implications and Challenges

The composite material (DFC) exhibits promising potential for CR adsorption. However, its large-scale application still faces several challenges. Despite its high adsorption efficiency and low raw material cost, the industrial implementation of DFC requires a comprehensive assessment of environmental and economic factors. Particularly, the thermal treatment and activation processes involved in synthesis may result in high energy consumption and carbon emissions, necessitating a thorough life cycle assessment. Furthermore, the cost-effectiveness of DFC depends on optimizing synthesis parameters to reduce production costs while maintaining superior performance. In practical wastewater treatment, factors such as competing ions and pH variations may influence adsorption efficiency, highlighting the need for pilot-scale studies. Additionally, the regeneration and reuse of DFC are critical for its long-term sustainability, as thermal or chemical regeneration may incur additional costs and lead to material degradation. Future research should focus on refining synthesis processes, exploring low-energy regeneration technologies, and conducting industrial-scale validation to enhance the feasibility of DFC in wastewater treatment applications.

5. Conclusions

This study prepared DFC and systematically evaluated their adsorption performance for CR. The findings demonstrated that under ideal circumstances (pH = 5, 318 K, and 50 mg dosage), DFC achieved a maximum adsorption efficiency of 3790.06 mg⋅g⁻¹. Even after five cycles of reuse, the removal rate exceeded 97%, demonstrating excellent adsorption and regeneration performance. Adsorption isotherm analysis showed that the adsorption characteristics of DFC conformed to the Langmuir model, mainly controlled by the monolayer adsorption mechanism. The average adsorption energy (E = 73.4 kJ⋅mol⁻¹), calculated from the D-R model, above 16 kJ⋅mol⁻¹, therefore verifying the dominant significance of chemisorption throughout this adsorption process. The kinetic study indicated that the DFC adsorption adhered to the pseudo-second-order kinetic model, so confirming that chemisorption was the predominant mechanism. The analysis of the Weber–Morris diffusion model showed that the DFC adsorption occurred in two stages: initially controlled by external diffusion, in which the boundary layer effect greatly influenced the adsorption rate, and later changing to internal diffusion control. This study offers a scientific foundation and innovative approaches for developing efficient, renewable adsorbent materials and applying them to dye wastewater treatment.