Identification of structural and optical properties and adsorption performance of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 nanoparticles for the removal of Congo red dye

Serine Kassem; Nour AlHajjar; Amani Aridi; Ramadan Awad

doi:10.1016/j.arabjc.2023.105477

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01 2023

:17;

105477

doi:

10.1016/j.arabjc.2023.105477

Identification of structural and optical properties and adsorption performance of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ nanoparticles for the removal of Congo red dye

Serine Kassem^{^a}, Nour AlHajjar^{^a}, Amani Aridi^{^b,^c,⁎}, Ramadan Awad^{^d}

a Physics Department, Faculty of Science, Beirut Arab University, Beirut, Lebanon

b Chemistry Department, Faculty of Science, Beirut Arab University, Beirut, Lebanon

c Inorganic and Organometallic Coordination Chemistry Laboratory, Faculty of Sciences I, Lebanese University, Hadath, Lebanon

d Physics Department, Faculty of Science, Alexandria University, Alexandria, Egypt

⁎Corresponding author at: Chemistry Department, Faculty of Science, Beirut Arab University, Beirut, Lebanon. a.aridi@bau.edu.lb (Amani Aridi)

Received: 2023-7-18, Accepted: 2023-11-22,

Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Abstract

In the field of industrial wastewater treatment, the application of highly efficient methods such as adsorption is crucial for effectively eliminating dangerous pollutants before effluent discharge. Ferrite nanoparticles (NPs) have emerged as promising candidates for the efficient and sustainable removal of dyes from wastewater. Therefore, the synthesis, characterization, and application of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ (0 ≤ x ≤ 0.04) NPs as adsorbents for the removal of Congo Red (CR) dye, is reported in this study. The X-ray powder diffraction (XRD) analysis has confirmed the purity of the samples with a very small amount of hematite phase. The prepared NPs have pseudo-spherical morphology and average particle size in the range of 12–17 nm. Furthermore, the elemental compositions, estimated from the energy dispersive x-ray (EDX) measurements, indicate the homogenous distribution of elements and the substitution of the Fe³⁺ host ions by the Ru³⁺ dopant. As the Ru content increases from 0.00 to 0.04, S_BET increases from 52.39 to 78.34 m²/g, respectively. Furthermore, the pore diameter, ranging between 12.71 and 18.38 nm, reveals the mesoporous nature of the prepared samples. The direct and indirect bandgap energy, calculated from UV–vis spectroscopy analysis, is in the range of 3.049–3.232 eV and 1.894–2.642 eV, respectively. The adsorption performance of the prepared NPs for the removal of CR dye solution was investigated by varying the contact time, adsorbent amount, pH, and reaction temperature. The adsorption process follows a second-order kinetic model and the NPs with x = 0.015 revealed the highest adsorption rate with a rate constant k₂ = 2.7 × 10⁻³ g.mg⁻¹.min⁻¹. The optimum experimental conditions, achieved in the presence of 80 mg of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ NPs at 308 K and in the acidic medium at pH = 3.45, result in the removal of 87.5 % of CR dye after 60 min. After applying several non-linear adsorption isotherm models, mainly Langmuir, Freundlich, and Temkin isotherms, the experimental data were well-fitted with the Temkin isotherm model. The strong interactive force between CR and (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ nanoparticles is revealed from the high value of b_T knowing that b_T = 182.91 J.mol⁻¹.

Keywords

(Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 nanoferrite

Adsorption

Congo red removal

Adsorption kinetics

Adsorption isotherm

1 Introduction

Nanoparticles (NPs) are defined as materials with dimensions ranging from 1 to 100 nm, ensuring at least one dimension falls within this range (Phan & Haes, 2019). The size and shape of nanomaterials play a crucial role in determining their physical and chemical properties (Mathew & Juang, 2007; Phan & Haes, 2019). Thus, scientists are dedicating significant efforts towards the fabrication and the precise control of nanomaterials' morphology and size (Ahmad et al., 2022; Mathew & Juang, 2007). Moreover, the unique combination of magnetic properties, tunability, stability, and multi-functionality makes ferrite NPs stand out among other nanoparticles, offering significant potential for various technological and biomedical applications (Hammad, 2022). Researchers have utilized ferrite nanoparticles in the removal of dyes and organic pollutants which are recognized as significant sources of pollution (Aljohani et al., 2023). Among several wastewater treatment methods, adsorption is considered the most effective method (Alrefaee et al., 2023; Zafar et al., 2022). It is widely used in several studies due to its simplicity, cost-effectiveness, and low secondary waste production (Almahri et al., 2023; Bashir et al., 2023; Bazrafshan et al., 2021 ). It is worth mentioning that the use of nanomaterials in the field of adsorption has garnered significant attention. This interest stems from their small particle size and high surface area (Ghamkhari et al., 2020).

According to the crystal structure and the chemical compositions, ferrites are divided into three different types: spinel, garnets, and hexaferrites (Mathew & Juang, 2007; Sarveena, 2021). The current research will shed light on the spinel ferrites nanoparticles that fall under the category of ceramic substances, composed of iron oxide blended with other metals adopting the general A²⁺Fe³⁺Fe₂O₄ formula (Bajorek et al., 2019). It is of significance that A represents a divalent cation such as nickel, zinc, barium, cadmium, cobalt, or manganese. Spinel ferrites find extensive applications in the fields of electronics, industry, and biomedicine. They are utilized in various areas such as permanent magnets, telecommunications devices, wastewater treatment, targeted drug delivery, cancer treatment, and magnetic resonance imaging (MRI) (Bajorek et al., 2019; Kefeni et al., 2020; Kefeni & Mamba, 2020 ). Generally, spinel ferrites nanoparticles may exhibit normal spinel structure, such as ZnFe₂O₄ and CdFe₂O₄, where all A²⁺ and the Fe³⁺ cations occupy the tetrahedral (A) sites and the octahedral (B) sites, respectively (Sarveena, 2021)_. Whereas in inverse spinel structure, A²⁺ ions occupy the octahedral sites while Fe³⁺ ions are distributed over both octahedral and tetrahedral sites and this can be found in NiFe₂O₄, CoFe₂O₄, and MgFe₂O₄ (Sarveena, 2021)_. In mixed spinel structure, both A²⁺ and Fe³⁺ ions are distributed over the tetrahedral and the octahedral sites (Sarveena, 2021). MnFe₂O₄ is an example of a material exhibiting a mixed spinel structure. The arrangement of Mn²⁺ and Fe³⁺ creates a unique crystal structure with distinctive properties, making MnFe₂O₄ suitable for various applications, including magnetic, electrical, and catalytic applications (Akhlaghi & Najafpour-Darzi, 2021; A. Kumar et al., 2023).

Recently, there has been significant attention focused on tri-metal ferrite NPs among the various investigated spinel ferrites. For instance, Ni_0.5Zn_0.5Bi_0.04Nd_xFe_1.96-xO₄ has been synthesized by the citrate precursor technique. The current–voltage variation showed a high order of resistivity that is useful in high-frequency applications (Taneja et al., 2022). Furthermore, Ni_0.4Cu_0.3Zn_0.3Fe₂O₄ doped with additive Nb₂O₅ showed varied structural properties and yielded soft ferrite entities with tailored magnetic and optical parameters (Patil et al., 2022). Moreover, Ni_0.7Mn_0.2Cu_0.1Fe_2-xAl_xO₄ nanoferrites were synthesized by sol–gel auto-combustion technique at room temperature. The prepared ferrites exhibited excellent crystal structure, enhanced initial permeability, and good strain size (Suresh et al., 2022). It is worth noting that these ferrite NPs can be synthesized using several methods, and the co-precipitation technique is the best one due to its possibility of controlling the size of the NPs and cost-effectiveness (Bazrafshan et al., 2023).

Studying the properties of cadmium-nickel-manganese ferrite NPs is highly interesting due to the diverse properties exhibited by each of CdFe₂O₄, NiFe₂O₄ and MnFe₂O₄ NPs. For example, in a previous investigation, CdFe₂O₄ was used as an effective adsorbent for the removal of Methylene Blue from an aqueous solution, and the removal percentage obtained was over 99 % (Vodă et al., 2016). Moreover, Ni_(1-x)Mn_xFe₂O₄ exhibited effective performance for the removal of Congo red dye (Luo et al., 2018). In addition, nickel ferrite nanocomposites were prepared using chemical co-precipitation and applied for efficient removal of phenol from the aqueous solutions by adsorption process, and more than 96 % of phenol was removed (Mohammadi et al., 2021). Furthermore, MnFe₂O₄ was used as an efficient adsorbent in the removal of Acid Red B dye from water (Wu & Qu, 2005).

Numerous investigations have been conducted to explore the characteristics of pure CdFe₂O₄, NiFe₂O₄, and MnFe₂O₄ nanoparticles. However, there is a noticeable gap in the existing literature concerning the properties of tri-metal ferrite nanoparticles. To address this gap, our study introduces a novel approach by synthesizing tri-metal ferrite nanoparticles known as Cd-Ni-Mn ferrite nanoparticles to enhance their properties. It is worth noting that this particular tri-metal ferrite nanoparticle has not been synthesized or studied previously. Additionally, by incorporating ruthenium (Ru) into the crystal structure, the properties of the resulting ferrite material can be further modified and tailored (Manikandan et al., 2019). Thus, this work reports the synthesis and characterization of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ (0.00 ≤ x ≤ 0.04) NPs. Furthermore, the effect of Ru dopant content on the structural and optical properties of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs was investigated. Besides, the adsorption performance of the prepared NPs for the removal of CR dye was studied.

2 Experimental techniques

2.1

2.1 Synthesis of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs

(Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs, where x = 0.00, 0.005, 0.01, 0.015, 0.02 and 0.04, were prepared via the coprecipitation method. The preparation involved the use of high-purity salt chloride precursors, including cadmium (II) chloride hemipentahydrate, nickel (II) chloride hexahydrate, manganese (II) chloride tetrahydrate, ruthenium (III) chloride hydrate and iron (III) chloride hexahydrate. The appropriate stoichiometric proportions of the salt chlorides were dissolved in deionized water to form cationic solutions. Then, the prepared cationic solutions were mixed and stirred for 30 min to improve the homogeneity of the solutions. After that, 4 M NaOH solution was added dropwise to obtain a solution with a pH value of 12. Then, the obtained mixture was heated at 80 °C for 2 h accompanied by continuous stirring. Afterward, a mixture of ethanol and deionized water was used to wash the precipitates until the pH of the filtrate solution dropped to 7. The precipitates were dried in the oven at 100 °C for 18 h. Lastly, grinding and the calcination of the NPs were carried out at 600 °C for 5 h (Aridi et al., 2021; Chehade et al., 2022).

2.2

2.2 Characterization of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs

The crystallinity, crystal structure, and phase purity of the synthesized NPs were examined by XRD using Bruker D8 Focus powder diffractometer with Cu-K_α radiation (λ = 1.5406 Å) where the diffraction angle 2θ ranges between 20° and 80°. The JEM-2100 was used for the TEM analysis to record the morphology and particle size distribution. To perform the EDX and SEM techniques, the JOEL scanning electron microscope (JSM-5300) was used. The textural properties were evaluated using the N₂ adsorption–desorption method via a 650 Full ISO therme physisorption analyzer. Moreover, the optical properties of the prepared NPs were determined using a JASCO V-670 spectrophotometer at room temperature in the range of 200–700 nm (Aridi et al., 2021).

2.3

2.3 Adsorption performance of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs

The adsorption performance of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs, where 0.00 ≤ x ≤ 0.04, was evaluated for the removal of CR dye. All adsorption tests are performed in the dark. To identify the sample with the best adsorption activity, 60 mg of each sample was mixed with 150 mL of 30 ppm CR dye solution. At a specified time interval, 3 mL was extracted from the reaction mixture and analyzed using a UV–vis spectrophotometer (Jasco V-650) to measure the remaining concentration of CR dye in the solution. The adsorbed quantity (q_e) of CR was calculated using the following equation (Samoila et al., 2015):

(1)

qe = \frac{(C_{0} - C_{e}) \times V}{m}

where C₀ (mg.L⁻¹) and C_e (mg.L⁻¹) denote the initial CR dye concentration and the dye concentration at equilibrium, respectively, V (L) is the volume of the CR solution, and m (g) is the mass of the adsorbent.

The sample that displayed the most efficient adsorption performance and exhibited the highest rate constant was selected for a comprehensive study to assess the impact of various factors on the adsorption rate. These factors included contact time, adsorbent amount (20, 40, 60, 80, and 100 mg), pH level (2.99, 3.45, 4.19, 7.46, 9.85, and 11.01), and temperature (303, 308, 313, 318 and 323 K) to investigate their influence on the adsorption reaction rate. It is important to note that the pH of the CR solution was adjusted by adding a few drops of 0.1 M HCl and NaOH. After conducting the adsorption reaction at various temperatures, the thermodynamic parameters were calculated, such as the change in the enthalpy (ΔH⁰), the change in the entropy (ΔS⁰) and the change in the Gibbs free energy (ΔG⁰). Subsequently, different adsorption isotherms mainly Langmuir, Freundlich, and Temkin isotherms, to determine the model that best fits the experimental data. It is worth noting that the adsorption experiments were conducted three times to ensure accuracy and reliability.

3 Results and discussion

3.1

3.1 XRD analysis

XRD was performed to determine the structure, crystallite size, and lattice parameter of synthesized NPs. The fitting of the XRD patterns of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs, represented in Fig. 1 (a), was performed by Rietveld refinement using the MAUD software. Major diffraction peaks are located at 2θ ≈ 29.8°, 35.1°, 42.7°, 52.9°, 56.4°, 61.9°, 69.9°, and 73.2°, corresponding to the (2 2 0), (3 1 1), (4 0 0), (4 2 2), (5 1 1), (4 4 0), (6 2 0) and (5 3 3) planes, respectively. This confirms the formation of the spinel cubic structure, with space group Fd3m (Basma et al., 2022). The refined structure of pure (Cd_0.4Ni_0.4Mn_0.2)Fe₂O₄ NPs, obtained from the Visualization of Electronics and Structural Analysis (VESTA) software, is shown in Fig. 1 (b). Furthermore, the extracted parameters from Rietveld refinement are listed in Table 1. The weighted profile R-factor (R_wp) and the expected R-factor (R_p) are approximately equal and the goodness of fit (χ² ̴ 1). Thus, the fitting is reliable and the patterns are well-refined. It is clear that a small fraction of the hematite phase (1.51 %) appears in the pure NPs. However, upon doping NPs with Ru³⁺ dopant, the percentage of hematite phases is reduced to 0.48 % for NPs with x = 0.04. This means that the dopants’ ions are successfully incorporated with the spinel ferrite without altering the crystal structure or producing a secondary phase. This result is comparable with a previous study that analyzed the XRD data of Ni_0.7Zn_0.3-Er_xFe_2-xO₄ NPs, and the intensity of the hematite phase decreased with the increasing of Er content (Sandeep et al., 2022).

(a) XRD patterns of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs where 0.00 ≤ x ≤ 0.04 and (b) refined structure of pure (Cd0.4Ni0.4Mn0.2)Fe2O4 NPs.

Table 1 Extracted parameters from Rietveld refinements of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ nanoparticles using MAUD software.

Ru content	x	0.00	0.005	0.01	0.015	0.02	0.04
Phase percentage %	(Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄	98.48	98.73	99.05	99.21	99.44	99.51
Phase percentage %	Fe₂O₃	1.51	1.26	0.94	0.78	0.55	0.48
*R_wp*		0.099	0.1	0.1	0.1	0.1	0.1
*R_exp*		0.08	0.08	0.079	0.08	0.08	0.08
χ²		1.23	1.24	1.27	1.28	1.38	1.28
Lattice parameter Å	a MAUD	8.462	8.454	8.467	8.455	8.469	8.482
Lattice parameter Å	a Bragg's law	8.472	8.471	8.489	8.469	8.490	8.492
M g/mol		255.115	255.341	255.567	255.793	256.019	256.924
ρ g/cm³		5.592	5.613	5.593	5.620	5.599	5.591

The lattice parameter a, listed in Table 1, was extracted from MAUD software and determined from Bragg’s Law as well. Both methods reveal the same trend. It’s observed that the lattice parameter increases from 8.462 to 8.482 Å as the Ru content (x) increases from 0.00 to 0.04. This increase is mainly attributed to the doping of NPs with Ru³⁺ ions having greater ionic radii (0.68 Å) compared to the ionic radii Fe³⁺ (0.65 Å) (Vinod et al., 2022). The values lattice parameter a followed an unsystematic variation with the increase of Ru content. As Ru content increases, the Ru³⁺ ions do not easily occupy their positions at A or B sub-lattice sites therefore some of the ions will be located at grain boundaries (Vinod et al., 2022). The X-ray density ρ, listed in Table 1, was determined using the following equation (Singh Yadav et al., 2018):

(2)

ρ = \frac{8 M}{N_{a} a^{3}}

where N_a is the Avogadro number, M is the molecular weight, and a is the lattice parameter. It’s obviously shown that the X-ray density is inversely proportional to the lattice parameter. The variation in the X-ray density might be attributed to the unremarkable variation between the lattice parameter and M (Basma et al., 2022).

To determine the crystallite size of the prepared NPs, the XRD profile analysis was carried out using three different methods, the Debye-Scherrer Model (D-S), the Uniform Deformation Model (UDM) from the Williamson-Hall approach, and the size-strain plot (SSP), the obtained results are listed in Table 2. In the first method the crystallite size D_D-S was calculated from the broadening of XRD peaks using the Debye-Scherrer formula for the Lorentzian peak (Kumar et al., 2015):

(3)

D_{D - S} = \frac{0.9 λ}{β c o s θ}

by which, β denotes the full width at half maximum (FWHM), λ is the X-ray wavelength (1.5406 Å), and 2θ is the angle of diffraction. The second method was calculating the D_UDM via the UDM from the Williamson-Hall method by applying the following equation (Chellab & Harbbi, 2019):

(4)

β \cos θ = (\frac{k λ}{D_{UDM}}) + (4 ε \sin θ),

where k is a dimensionless shape factor, with a value close to unity and ε is the lattice strain. The practical application of this method involves plotting βcosθ against 4sinθ, knowing that the lattice strain ε and D_UDM are estimated from the slope and intercept, respectively (Chellab & Harbbi, 2019). The third method, used to calculate D_SSP, is the Size-Strain plot method as represented in the following equation (Chellab & Harbbi, 2019):

(5)

{(d_{hkl} β \cos θ)}^{2} = (\frac{k}{D_{SSP}}) (d_{hkl}^{2} β \cos θ) + {(\frac{ε}{2})}^{2},

The change in the values of the crystallite size, in the three methods, is due to the substitution of Ru³⁺ ions for Fe³⁺ ions on the lattice sites. By which, the ionic radii of Ru³⁺ ion (0.68 Å) is large as compared to Fe³⁺ ion (0.645 Å). So, it’s not easy to replace Fe³⁺ ions on the lattice site, and thus some of the Ru³⁺ ions may reside on the grain boundaries (Thakur et al., 2016).

Table 2 Crystallite size, strain, and R² values of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs determined by Debye-Scherrer (D_D-S), Uniform Deformation Model (D_UDM), and size-strain plot (D_SSP) Models.

x	0.00	0.005	0.01	0.015	0.02	0.04
D_D-S nm	8.861	6.017	5.411	8.130	6.036	5.520
D_UDM nm	21.331	18.243	14.148	14.005	19.528	20.094
Strain UDM	0.0065	0.0096	0.0089	0.0049	0.0092	0.0132
R² UDM	0.9115	0.9054	0.6483	0.8404	0.6596	0.9586
D_SSP nm	14.754	10.588	10.112	9.890	10.344	17.647
Strain SSP	0.0022	0.0031	0.0035	0.0015	0.0027	0.0045
R² SSP	0.9351	0.9092	0.8646	0.9774	0.7861	0.9015

The crystallite size determined from UDM and SSP models follows the same trend with increasing the Ru content. However, the high values of the coefficient of determination R² obtained from the SSP method ensure the well-fitting and reliability of this method. Thus, SSP is the best method for determining the crystallite size of the prepared NPs.

3.2

3.2 TEM, HRTEM and SAED analysis

Fig. 2 displays the TEM images and histograms, representing the particle size distribution, of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs where 0.00 ≤ x ≤ 0.04. From the obtained TEM images, it is clear that the prepared samples exhibit pseudo-spherical morphology. Similar morphology was observed in a previous study, where the samples Bi₂Fe_4-xMn_xO₉ (0.0 ≤ x ≤ 1.0) were synthesized using the co-precipitation route (Pooladi et al., 2019). The average particle size has been calculated using ImageJ software. The mean particle size ranges between 12.8 and 17.8 nm. The particle size decreases from 15.62 nm to 12.87 nm as x increases from 0 to 0.015, and then it increases to reach 17.85 nm as x increases from 0.02 to 0.04. The variation of the particle size analyzed from TEM results is in good agreement with the variation of crystallite size D_SSP obtained from XRD analysis. Nevertheless, the average particle size values obtained from TEM analysis are greater than those evaluated from XRD. This may be due to the fact that particle is formed from the agglomeration of crystallites (Seyring et al., 2009). It is known that agglomeration may occur due to the magnetic interactions among several energetic crystallites found in the ferrite NPs samples (Ansari et al., 2023).

(a-f) TEM, (g-l) particle size distribution, (m-r) HRTEM, and (s-x) SAED images of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 nanoparticles where x = 0.00, 0.005, 0.01, 0.015, 0.02 and 0.04.

From the HRTEM images, shown in Fig. 2 (m-r), the interplaner distance was calculated using ImageJ software. The interplanar distance corresponding to the (3 1 1) plane of the most intense peak appears in HRTEM images of all the prepared samples. Thus, the HRTEM result is in good agreement with the XRD analysis. Furthermore, the selected area electron diffraction patterns (SAED) of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs for the six samples are also shown in Fig. 2 (s-x). These SAED patterns consist of rings having bright spots over them that ensure the polycrystalline nature of the samples (Debnath & Das, 2020). The lattice fringes enabled the calculation of the d-spacing using ImageJ software. The calculation of d-spacing gave information about the plane types. In other words, the Miller indices were determined using the values of d-spacing, and thus the average lattice parameter was calculated for each sample. As shown in Fig. 3, the values lattice parameter calculated from SAED patterns is well matched with the results of XRD patterns. As Ru³⁺ content increased from 0 to 0.04, the lattice parameter obtained from MAUD software increased from 8.462 to 8.482 Å, and the values obtained from SAED images increased from 8.69 to 8.77 Å.

Variation of lattice parameter (aXRD and aSAED) for (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs where x = 0.00, 0.005, 0.01, 0.015, 0.02, and 0.04.

3.3

3.3 SEM and EDX analysis

Scanning electron microscopy (SEM) was used to study the morphology and size of pure and Ru-doped ferrite NPs. The SEM images, shown in Fig. 4, reveal the pseudo-spherical morphology of the prepared NPs. The agglomeration might be attributed to the magnet-dipole interactions among the NPs (Köseoğlu, 2015). As a result of the enhanced aggregate of the prepared NPs, the synthesized samples may exhibit excellent electrical properties (Vinod et al., 2022). The grain sizes of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs, estimated from SEM analysis, are 32.7, 29.6, and 30.7 nm for x = 0.00, 0.02, and 0.04, respectively. The variations of the obtained grain size from SEM analysis are similar to those estimated from XRD and TEM results. However, the values of particle size are greater than the values obtained from XRD and TEM analysis. Thus, this validates the TEM discussion.

SEM images and EDX spectra of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs where (a) x = 0.00, (b) x = 0.02 and (c) x = 0.04.

The energy dispersive X-ray spectroscopy (EDX) was carried out to study and validate the elemental compositions of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs where x = 0.00, 0.02, and 0.04. The EDX spectra, represented in Fig. 4, revealed peaks related to the presence of Cd, Ni, Mn, Fe, O, and Ru. Thus, the purity of the prepared NPs (x = 0.00, 0.02, and 0.04) was confirmed by the absence of extra peaks in the EDX spectra. The quantitative EDX analysis was performed by which the atomic percentage of elements was calculated at different regions in the sample and compared with the theoretical values as listed in Table 3. The results indicate that the elements are homogeneously distributed. The experimental atomic percentage of the elements was comparable with the theoretically calculated values. Furthermore, the substitution of the host Fe³⁺ ions by the guest Ru³⁺ ions is identified from the decrease in the atomic percentage of Fe from 27.42 to 24.33 % accompanied by the increase in the atomic percentage of Ru from 0 to 0.47 % as the Ru content increases from 0 to 0.04 as shown in Fig. 5. It is important to note that the EDX analysis was done on 3 different regions for each sample. The regions analyzed for pure (Cd_0.4Ni_0.4Mn_0.2)Fe₂O₄ NPs are displayed in Fig. 6. Furthermore, the obtained atomic % for each element in (Cd_0.4Ni_0.4Mn_0.2)Fe₂O₄ NPs are listed in Table 4. It is clear that a similar atomic % is revealed for each element in the analyzed regions. Consequently, the composition and distribution of elements within the nanoparticles are uniform and not significantly varied in different areas of the sample.

Table 3 Experimental and theoretical values of the atomic percentages of the elements of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs.

x	0.00	0.02	0.04
Experimental atomic percentage %
Cd	5.18 $\pm$ 0.07	5.55 $\pm$ 0.11	5.07 $\pm$ 0.10
Ni	5.02 $\pm$ 0.17	5.42 $\pm$ 0.19	4.94 $\pm$ 0.17
Mn	2.85 $\pm$ 0.09	2.67 $\pm$ 0.12	2.54 $\pm$ 0.11
Fe	26.61 $\pm$ 0.33	26.35 $\pm$ 0.23	24.33 $\pm$ 0.30
Ru	0	0.20 $\pm$ 0.04	0.47 $\pm$ 0.03
O	60.36 $\pm$ 0.04	60.36 $\pm$ 0.78	63.09 $\pm$ 0.73
Theoretical atomic percentage %
Cd	5.7	5.7	5.7
Ni	5.7	5.7	5.7
Mn	2.8	2.8	2.8
Fe	28.5	28.2	28
Ru	0	0.28	0.57
O	57.14	57.14	57.14

Variation of the atomic % of host (Fe) and guest (Ru) with Ru content.

SEM images and EDX spectra of (Cd0.4Ni0.4Mn0.2)Fe2O4 NPs in 3 different regions.

Table 4 Atomic percentages of the elements of (Cd_0.4Ni_0.4Mn_0.2)Fe₂O₄ NPs in 3 different regions.

Experimental atomic percentage %
	Cd	Ni	Mn	Fe	O
Region 1	5.36	5.03	3.02	26.21	60.38
Region 2	5.12	5.37	2.90	26.19	60.42
Region 3	5.06	4.66	2.65	27.42	60.27

3.4

3.4 Surface area measurement

The N₂ adsorption–desorption isotherms of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs where x = 0.00, 0.015 and 0.04 along with pore size distribution curves are shown in Fig. 7. According to the IUPAC classification, the N₂ adsorption–desorption isotherms display a type IV isotherm. Brunauer–Emmett–Teller (BET) analysis is applied to examine the surface area (S_BET). Whereas the pore volume and diameter are determined via the Barrett–Joyner–Halenda (BJH) model. The values of S_BET, pore volume, and pore diameter are listed in Table 5. As the Ru content increases from 0.00 to 0.04, S_BET increases from 52.39 to 78.34 m²/g, respectively. Among the analyzed samples, (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs where x = 0.015 exhibit the highest pore volume and diameter. The pore diameter, ranging between 12.71 and 18.38 nm, reveals the mesoporous nature of the prepared samples (Aridi et al., 2023).

N2 adsorption–desorption isotherms along with pore size distribution curves of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs where (a) x = 0.00, (b) x = 0.015, and (c) x = 0.04.

Table 5 Surface area (S_BET), pore volume, and pore diameter of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs with x = 0.00, 0.015, and 0.04.

x	S_BET (m²/g)	Pore volume (cm³/g)	Pore diameter (nm)
0.00	52.39	0.32	16.81
0.015	54.15	0.39	18.38
0.04	78.34	0.26	12.71

3.5

3.5 UV–vis analysis

The UV–vis absorbance spectra, represented in Fig. 8, were recorded at room temperature from 250 to 700 nm to estimate the optical bandgap energy of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs. A major absorption peak appears in the range between 335 and 338 nm. The results of the maximum peak position are comparable with previous results at which the absorbance peak was around 300 nm (Sattar et al., 2022).

UV absorption spectra of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4NPs.

Knowing that, the highest absorbance peak of nanosized magnetic materials takes a position in the range of 200–350 nm (Sattar et al., 2022). The bandgap energy of the prepared NPs was estimated by several methods and the results are listed in Table 6. Tauc’s equation was used to determine the bandgap energy and is represented as follows (Vinod et al., 2022):

(6)

{(α h ν)}^{n} = B (h ν - E_{g}),

where hν is the photon energy, B is the transition probability dependence constant, E_g is the optical bandgap energy and n is the nature of transition (where n = 2 and

1 / 2

denote the direct and indirect transitions). The following relation was used to calculate the absorption coefficient (α) (Vinod et al., 2022):

(7)

α = \frac{2.303 A}{d}

where A is the absorbance and d is the light path length (1 cm). Direct and indirect transitions were calculated by plotting

{(α h ν)}^{n}

vs. E as shown in Fig. 9. As listed in Table 5, the direct and indirect bandgap energy is in the range of 3.049–3.232 eV and 1.894–2.642 eV, respectively. The obtained results are comparable to those reported in previous studies (Abdel Maksoud et al., 2021; Bhalla et al., 2021). Besides, the direct bandgap energy can be calculated by another method using the following formula:

(8)

E = \frac{h c}{λ}

where h is Planck's constant, c denotes the speed of light and λ is the wavelength obtained from Fig. 10 (a). The Urbach energy (E_U) determines the band tail width of localized states within the bandgap of NPs and can be calculated using this formula (Lemziouka et al., 2020):

(9)

\ln (α) = \ln (α_{0}) + \frac{h ν}{E_{U}}

Knowing that α₀ is a constant. The Urbach energy, calculated from the reciprocal of the slope of the linear plot of ln (α) vs. (hν), is demonstrated in Fig. 10 (b). The obtained E_U values range between 0.44 and 0.99 eV. Comparable results were reported in previous studies, by which, the E_U values range between 0.42 and 0.6 eV (Lemziouka et al., 2020). The decrease in the E_U from 0.99 to 0.73 eV as the Ru³⁺ content increased from 0.00 to 0.04 was due to the decrease in the defects found in the doped samples (Bagi Aljewaw et al., 2020; Fernández et al., 2023).

Table 6 Values of direct and indirect bandgap energy and Urbach energy (E_U) of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs.

x	E_g Tauc’s plot eV	E_g Absorbance eV	E_gi eV	E_U eV
0.00	3.049 ± 0.009	2.796 ± 0.004	1.894 ± 0.001	0.992 ± 0.009
0.005	3.215 ± 0.003	2.876 ± 0.002	2.572 ± 0.005	0.487 ± 0.005
0.01	3.223 ± 0.002	2.927 ± 0.003	2.606 ± 0.009	0.465 ± 0.004
0.015	3.232 ± 0.003	2.934 ± 0.002	2.642 ± 0.005	0.441 ± 0.004
0.02	3.221 ± 0.003	2.928 ± 0.002	2.598 ± 0.004	0.470 ± 0.009
0.04	3.128 ± 0.004	2.895 ± 0.003	2.214 ± 0.007	0.739 ± 0.002

Tauc’s plot of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs used to determine (a) direct bandgap energy and (b) indirect bandgap energy.

Plot of (a) direct bandgap energy determined from absorbance and (b) Urbach energy (EU) determination of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs.

The values of direct bandgap energy E_g and indirect bandgap energy E_gi increase as the Ru content increases from 0.00 to 0.015. Whereas, with further increase in Ru content (x = 0.04), the values of E_g and E_gi reduced. This variation of E_g and E_gi depends on the particle size, crystallinity, lattice constant, lattice strain, purity, nature and compositions of the dopant (Patade et al., 2020). It is worth mentioning that all doped NPs revealed lower E_U values compared to pure NPs. Thus, the defects are diminished upon doping NPs with Ru. This is in good agreement with XRD results that showed a reduction in the secondary phase percentage upon doping NPs. An inverse relation between E_g and E_U is observed as displayed in Fig. 11. Moreover, the E_g values are inversely proportional to the crystallite and particle size obtained from XRD and TEM analysis, respectively. This relationship is attributed to the variation of the lattice constant and quantum confinement effect (Singh et al., 2010). The reason behind the increase in the bandgap energy from 3.049 to 3.232 eV with the reduction in the particle size from 15.6 to 12.8 nm as x increases from 0 to 0.015 is owed to the quantum size effect (Zhou et al., 2019). Knowing that the quantum size effect leads to a blue shift of bandgap energy (Singh et al., 2010). Conversely, as x increases from 0.015 to 0.04, the particle size increases from 12.8 for to 17.8 nm whereas the energy gap decreases from 3.232 to 3.128 eV. This decrease is attributed to surface and interface effects, resulting in a red shift of the bandgap energy (Singh et al., 2010). Furthermore, similar results for the variation of particle size with the energy gap were observed in previous studies about Mn_1-xZn_xFe₂O₄ where the bandgap energy increased with the increase in zinc concentration and the crystallite size decreased (Patade et al., 2020).

Variation of bandgap energy and particle size (DTEM and DSSP) of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs where 0.00 ≤ x ≤ 0.04.

3.6

3.6 Adsorption performance of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs

Doping ferrite NPs with rare earth metals improves their properties and enhances their effectiveness in wastewater treatment, particularly in dye removal (Ahmed et al., 2014; Keerthana et al., 2021). Therefore, the capability of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs in the adsorption of CR dye was studied. For this study, 60 mg of each of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs, where 0.00 ≤ x ≤ 0.04, was mixed with 150 mL of 30 ppm CR solution. The pseudo-first-order and pseudo-second-order models were used to study the adsorption kinetics of CR and the obtained equation and R² values are listed in Table 7. The first-order kinetic model is given as follows (Konicki et al., 2013):

(10)

\ln (q_{e} - q_{t}) = - k_{1} t + \ln (q_{e})

where k₁ represents the rate constant (min⁻¹) and q_e (mg.g⁻¹) and q_t (mg.g⁻¹) represent the amount of CR dye adsorbed at equilibrium and at any time t, respectively. Moreover, the second-order model is given as follows (Konicki et al., 2013):

(11)

\frac{t}{q_{t}} = \frac{1}{k_{2} q_{e}^{2}} + \frac{t}{q_{e}}

where k₂ represents the second-order rate constant (g.mg⁻¹.min⁻¹). Therefore, q_e and k₂ were determined from the slope and intercept of the plot of t/q_t versus t, respectively. Higher R² values (R²

\geq

0.972) were obtained from second-order kinetics compared to the first-order model. This suggests that the adsorption of CR dye is well described by the second-order kinetics model. Similar results were reported in previous studies where the pseudo-second-order kinetic model was used to describe the adsorption process of CR dye on the CaFe₂O₄ and MFe₂O₄ (M = Co²⁺, Mg²⁺, Mn²⁺, Ni²⁺, Zn²⁺) (Liu et al., 2015; Roman et al., 2019). Therefore, the variation of the rate constant (k₂) of the adsorption reaction with the Ru content is represented in Fig. 12. Among the prepared NPs, the highest adsorption rate was achieved in the presence of (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs with x = 0.015. It is worth mentioning that (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs with x = 0.015 have the smallest particle size as revealed from XRD and TEM analysis. The surface area, pore size, and volume play a significant role in adsorption. Furthermore, (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs with x = 0.015 revealed high pore diameter and volume. Thus, larger pores provide more space for adsorbate molecules to be captured and retained, contributing to better performance. (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs with x = 0.015 have been used to examine the influence of contact time, adsorbent amount, pH and temperature on the adsorption rate of CR dye.

Table 7 Equations and R² values of first-order and second-order kinetics.

x	First-order kinetics		Second-order kinetics
x	Equation	R²	Equation	R²
0	y = −0.0179x + 3.5024	0.688	y = 0.0155x + 0.2219	0.972
0.005	y = −0.0176x + 3.5451	0.929	y = 0.0136x + 0.1184	0.997
0.01	y = −0.0196x + 3.7622	0.947	y = 0.0127x + 0.1416	0.995
0.015	y = −0.0079x + 3.1322	0.737	y = 0.0181x + 0.1212	0.986
0.02	y = −0.0133x + 3.0767	0.945	y = 0.0197x + 0.1532	0.997
0.04	y = −0.0025x + 3.5635	0.630	y = 0.0133x + 0.0895	0.997

The adsorption rate constant (k2) of CR in the presence of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs where x = 0.00, 0.005, 0.015, 0.02, and 0.04.

The first-order and second-order kinetics models do not provide an adequate explanation of the diffusion mechanism. Therefore, the intraparticle diffusion (IPD) model was employed in order to gain insights into the adsorption mechanism of CR dye onto (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs. The equation of the IPD model is expressed as follows (Zafar et al., 2022):

(12)

q_{t} = k_{i} t^{1 / 2} + C

where k_i (mg. g⁻¹.min^1/2) is the IPD rate constant and C (mg. g⁻¹) is the intercept. The values of k_i and C are listed in Table 8. The IPD model plot, displayed in Fig. 13, reveals the coexistence of two linear regions in the presence of all samples. Thus, multiple stages are involved in the adsorption process of CR. Furthermore, the k_i values of the first region are greater than that of the second region. The first region in the IPD plot reveals the rapid diffusion stage accompanied by external surface adsorption of CR. However, the second region in the plot shows a slow adsorption stage which is associated with the intraparticle diffusion of CR within the pores of the (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ adsorbents (Zafar et al., 2022). Since C exhibits non-zero values, intra-particle diffusion is not the only rate-limiting step (Lafi et al., 2019). The contribution of surface adsorption is revealed from the large values of C.

Table 8 Values k_i and C estimated from linear regions of the IPD model plot.

x		0.00	0.005	0.01	0.015	0.02	0.04
Region 1	k_i	3.03	3.35	7.15	3.02	2.73	3.81
Region 1	C	25.37	33.58	12.02	26.38	20.87	28.91
Region 2	k_i	1.76	1.44	1.12	1.11	1.16	2.04
Region 2	C	36.19	50.04	59.39	40.17	32.53	44.19

IPD model applied on CR adsorption data in the presence of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 (0.00 ≤ x ≤ 0.04) adsorbents.

3.6.1

3.6.1 Effect of contact time

By determining the optimal contact time, the efficiency of dye removal will be improved and the wastewater treatment time will be minimized. The influence of the contact time on the removal % of CR was studied by mixing 60 mg of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent with 150 mL of 30 ppm CR dye solution. The removal %, displayed in Fig. 14, was determined using the following formula:

(13)

R e m o v a l % = \frac{C_{0} - C_{t}}{C_{0}} \times 100,

As the contact time increases to 180 min, the removal percentage also increases until it reaches 59.03 %. With a further increase in the contact time, the removal % increases to reach 60.73 % after 240 min. A comparison with previous studies, as listed in Table 9, clearly indicates that the synthesized nanoparticles (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ have the highest removal % and adsorption capacity. This reveals that (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ is an effective adsorbent for the removal of CR dye. Consequently, tri-metal ferrite nanoparticles exhibit superior adsorption performance compared to single and di-metal ferrite nanoparticles.

Effect of contact time on the removal of CR examined in the presence of (Cd0.4Ni0.4Mn0.2)Fe1.985Ru0.015O4 adsorbent.

Table 9 Previous studies about the removal of CR dye in the presence of different ferrite nanoparticles.

Adsorbent	Adsorbent Dosage	CR dye concentration ppm	Removal %	Time min	qe mg/g	Ref.
Cu_0.5Co_0.5Fe₂O₄	50 mg	100	–	–	38.69	(Liu et al., 2022)
MnFe₂O₄	100 mg	30	–	–	7.497	(Yang et al., 2014)
NiFe₂O₄	50 mg	30	–	–	38.47	(Tan et al., 2020)
MnFe₂O₄	0.05 g/L	62	–	–	11.1	(Pham, T. Nguyen, et al., 2019)
NiFe₂O₄ ZnFe₂O₄ CoFe₂O₄	1 g/L	50	23 % 12 % 19 %	180	11.4 5.1 10	(Samoila et al., 2015)
(Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄	60 mg	30	59.03 60.73	180 240	56.95	This study

3.6.2

3.6.2 Effect of adsorbent amount

To examine the influence of adsorbent amount on the rate of CR adsorption, various amounts (20, 40, 60, 80, and 100 mg) of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent were mixed with 150 mL of 30 ppm CR dye solution. Following the second-order kinetic model, the adsorption rate (k₂) was calculated. The results, displayed in Fig. 15, reveal that the rate constant is boosted from 0.75 × 10⁻³ to reach 5.51 × 10⁻³ g.mg⁻¹.min⁻¹ as the adsorbent amount increases from 20 to 80 mg, respectively. This is due to the increase in the active sites upon increasing the adsorbent amount (Ali et al., 2018; Zeng et al., 2014). Whereas, the rate constant is diminished from 5.51 × 10⁻³ to reach 3.65 × 10⁻³ g.mg⁻¹.min⁻¹ as the adsorbent amount increased from 80 to 100 mg. This might be attributed to the aggregation of NPs in the presence of excess amounts leading to a reduction in the adsorption efficiency (Panda et al., 2021). Therefore, 80 mg is the optimum adsorbent amount of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ used for the adsorption of CR dye.

Effect of (Cd0.4Ni0.4Mn0.2)Fe1.985Ru0.015O4 adsorbent amount on adsorption rate of CR dye.

3.6.3

3.6.3 Effect of pH

Varying pH is an important factor that has a vital role in the adsorption mechanism since it regulates the charges of the adsorbent that affect the interaction with dye molecules (Wekoye et al., 2020). Consequently, the adsorption experiments have been carried out in the presence of 60 mg of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent in different mediums where the pH ranges between 2.99 and 11.01. The obtained results are displayed in Fig. 16. The rate constant of the adsorption reaction increased from 2.12 × 10⁻³ to 4.81 × 10⁻³ g.mg⁻¹.min⁻¹ as the pH increased from 2.99 to 3.45. Whereas, as the pH increased from 3.45 to 11.01, the rate constant decreased to reach 1.33 × 10⁻³ g.mg⁻¹.min⁻¹. As documented in previous studies, the point of zero charge (PZC) of CdFe₂O₄, NiFe₂O₄, and MnFe₂O₄ NPs were 6.9, 6.4, and 6.5 respectively (Douafer et al., 2023; Eyvazi et al., 2020; Fathy et al., 2022 ). It is known that NPs are positively charged in an acidic medium (pH < PZC) and negatively charged in a basic medium (pH > PZC) (An et al., 2015; Chauhan et al., 2020). Furthermore, CR, as an anionic dye, carries a negative charge. Thus, the electrostatic attraction between the positively charged NPs and the negatively charged CR dye molecules promotes adsorption which in turn boosts the rate of the adsorption process in an acidic medium (Sachin et al., 2023). In other words, opposite charges attract each other, leading to a higher affinity and stronger binding between the dye and the positively charged NPs. Conversely, at high pH, when both the dye and the NPs’ surface carry negative charges, the repulsion between them reduces the adsorption capacity and weakens the adsorption process (Chauhan et al., 2020). Thus, the highest adsorption rate of CR is achieved in an acidic medium where pH = 3.45. The results are comparable to previous studies that studied the adsorption capacity of cobalt ferrite NPs for the removal of CR dye at different pH values, and the highest adsorption rate was achieved in an acidic medium (Simonescu et al., 2021). By this, the adsorption capacity increased as the pH value increased to 4.5, and then it decreased as the pH increased up to 11.

Effect of pH on rate constant (k2) of CR dye adsorption examined in the presence of (Cd0.4Ni0.4Mn0.2)Fe1.985Ru0.015O4 adsorbent.

3.6.4

3.6.4 Effect of temperature

To understand the temperature dependence, adsorption experiments were conducted at different temperatures (303, 308, 313, 318, and 323 K) in the presence of 60 mg of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent mixed with 150 mL of 30 ppm CR dye solution. The results, displayed in Fig. 17, show that as the reaction temperature increased from 303 to 308 K, the rate constant increased from 1.74 × 10⁻³ to 4.52 × 10⁻³ g.mg⁻¹.min⁻¹. It’s worth noting that increasing the reaction temperature provides energy, increases the mobility of the NPs and dye molecules, and improves the adsorption process (Panda et al., 2021). However, the rate constant is diminished to 1.43 × 10⁻³ g.mg⁻¹.min⁻¹ with a further increase in the reaction temperature to reach 323 K. Thus, increasing the reaction temperature above 308 K is unfavorable for CR adsorption. This result is comparable to previous studies where 35 °C represents the best temperature for the removal of CR dye from an aqueous system (Muedi et al., 2022).

Effect of temperature on rate constant (k2) of the adsorption process of CR dye using (Cd0.4Ni0.4Mn0.2)Fe1.985Ru0.015O4 adsorbent.

3.6.5

3.6.5 Thermodynamic parameters

Thermodynamic parameters can be determined by conducting adsorption experiments at different temperatures. These parameters include the change in the enthalpy (ΔH⁰), the change in the entropy (ΔS⁰) and the change in the Gibbs free energy (ΔG⁰) and are listed in Table 10. The following equations are used to determine the thermodynamic parameters (Etemadinia et al., 2019; Subbaiah Munagapati et al., 2022):

(14)

\ln K_{d} = - \frac{{Δ H}^{0}}{R T} + \frac{{Δ S}^{0}}{R}

(15)

K d = \frac{C_{ac}}{C_{e}}

(16)

Δ G^{0} = - R T \ln K_{d}

where K_d represents the equilibrium constant, C_ac (mg/L) is the adsorbed concentration of CR at equilibrium, C_e (mg/L) denotes the equilibrium concentration of CR in the solution, R represents the universal gas constant (8.314 J.mol⁻¹.K⁻¹) and T represents the temperature in (K). By plotting ln K_d versus 1/T, represented in Fig. 18, ΔH⁰ and ΔS⁰ can be calculated from the slope and intercept, respectively. The negative value of ΔH⁰ indicates that the adsorption reaction is exothermic which is in agreement with the decrease in the rate constant as the temperature increases (Zeng et al., 2014). The adsorption process of CR onto (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ nanoparticles is characterized by the heat of reaction of 14.02 kJ.mol⁻¹ revealing that the adsorption process involves physisorption (Subbaiah Munagapati et al., 2022). Besides, the negative value of ΔS⁰ reveals the disorder decreases during the adsorption reaction. In addition, the negative values of ΔG⁰ reflect the spontaneous adsorption of CR dye on the (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ nanoparticles. These results are in good agreement with previously reported studies that show negative values of thermodynamic parameters for the removal of CR dye using Ni_0.6Fe_2.4O₄ nanoparticles (Zeng et al., 2014).

Table 10 Thermodynamic parameters for the adsorption reaction of CR dye in the presence of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent.

T K	ΔH⁰ kJ.mol⁻¹	ΔS⁰ J.mol⁻¹.K⁻¹	ΔG⁰ J.mol⁻¹
308	−14.02	−26.94	−5683.84
313			−5648.78
318			−5430.89
323			−5305.12

3.6.6

3.6.6 Optimum experimental conditions for CR adsorption

Optimized experimental conditions, mainly optimum adsorbent amount, pH, and temperature, were applied. To do so, 80 mg of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ NPs were mixed with 150 mL of 30 ppm CR dye solution having a pH value of 3.45 at 308 K. The results of this experiment, carried out three times, are represented in Fig. 19. The adsorption rate constant was estimated to be 10.7 × 10⁻³ g.mg⁻¹.min⁻¹. Furthermore, 87.5 and 88.5 % of CR dye was adsorbed after 60 and 120 min, respectively, as displayed in Fig. 19 (b). Thus, the rate of adsorption reaction and removal % are boosted upon applying the optimum experimental conditions.

(a) The kinetic plot of t/qt and (b) removal % versus time in the presence of (Cd0.4Ni0.4Mn0.2)Fe1.985Ru0.015O4 adsorbent.

3.6.7

3.6.7 Reusability of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent

The regeneration ability and stability of adsorbents are key factors in determining the practicality and effectiveness of adsorption processes (Hassan et al., 2020; M. El-Metwaly et al., 2022). It promotes sustainability, cost-effectiveness, and resource conservation while enabling more efficient and reliable industrial processes and environmental remediation efforts (Almahri et al., 2023a). Therefore, the reusability of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent was studied by applying the optimum experimental conditions for 4 consecutive cycles. As displayed in Fig. 20, around 87.5 % of CR dye was adsorbed in the 4 cycles. The adsorbent regeneration was achieved by rinsing first with ethanol solution and then with water (Pham et al., 2019). Knowing that (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbents are magnetic nanoparticles, they were easily separated from the solution by using a magnet without the need for centrifugation or filtration. (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent can be used for up to 4 cycles without a significant loss in the adsorption activity as demonstrated in Fig. 20. Thus, the prepared adsorbent preserves an improved adsorption activity after reusing.

Reusability of (Cd0.4Ni0.4Mn0.2)Fe1.985Ru0.015O4 adsorbent for CR adsorption.

3.6.8

3.6.8 Adsorption isotherms

Adsorption isotherms are essential to determine the adsorption phenomena between the CR dye and (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent, and to gain more insight about the mechanism of adsorption. Therefore, the adsorption reaction was carried out in the presence of 60 mg of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent with varying the initial concentration of CR dye (0–50 ppm). For the present adsorbate-adsorbent system, three non-linear adsorption isotherms models were used mainly Langmuir, Freundlich, and Temkin and the results are displayed in Fig. 21. The Langmuir adsorption isotherm model proposed that there is no intermolecular interaction between the adsorbate molecules and the adsorption of CR dye molecule is only limited to monolayer (Li et al., 2021; Mishra et al., 2020). In other words, adsorption occurs only when the dye molecules collide with the surface of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent (Li et al., 2021). The equation of Langmuir isotherm (Simonescu et al., 2021):

(17)

q_{e} = \frac{Q_{L} b_{L} C_{e}}{1 + b_{L} C_{e}},

where Q_L (mg.g⁻¹) is the maximum monolayer adsorption capacity of (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ adsorbent and b_L (L.mg⁻¹) is the Langmuir constant. Regarding the Freundlich adsorption isotherm model, it is valid for heterogeneous surface of adsorption and describes multilayer adsorption with the interaction between the adsorbed molecules (Aridi et al., 2023). The equation is expressed as follows (Simonescu et al., 2021):

(18)

q_{e} = K_{F} {C_{e}}^{1 / n}

where n represents the adsorption intensity and K_F ((mg.g⁻¹)(mg.L⁻¹)^1/n) denotes the Freundlich constant. The Temkin model takes into account adsorbate-adsorbent molecules interaction (Mishra et al., 2020). The equation of this model is expressed as follows (Liu et al., 2022):

(19)

q_{e} = \frac{R T}{b_{T}} \ln (K_{T} C_{e})

where b_T (J.mol⁻¹) represents the coefficient related to the sorption heat, and K_T (L.mg⁻¹) represents the equilibrium binding constant. The values of the adsorption isotherm parameters determined via nonlinear fitting of Langmuir, Freundlich, and Temkin models along with the coefficient of determination (R²) are listed in Table 11. The low R² value obtained for the Langmuir model (R² = 0.682) along with the negative value of the Langmuir constant (b_L = -1.55) reveals the failure of this model in explaining the adsorption. However, greater R² values were obtained from the fitting of the Temkin model (R² = 0.979 compared to that of the Freundlich model (R² = 0.957). Thus, the Temkin model was the best-fitted model to explain the mechanism of the multilayer adsorption of CR onto (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ nanoparticles. Additionally, the adsorption intensity value (n = 3.72) is below 10 which signifies that the adsorption of CR is favorable. The strong interactive force between CR and (Cd_0.4Ni_0.4Mn_0.2)Fe_1.985Ru_0.015O₄ nanoparticles is revealed from the high value of b_T. Similar results were obtained in a previous study where the Temkin model best fitted the adsorption data of CR onto Cu_0.5Co_0.5Fe₂O₄ nanoparticles (Liu et al., 2022).

The fitting plots of non-linear Langmuir, Freundlich, and Temkin isotherm for adsorption of CR on (Cd0.4Ni0.4Mn0.2)Fe1.985Ru0.015O4 adsorbent.

Table 11 Adsorption isotherm parameters determined via nonlinear fitting of Langmuir, Freundlich, and Temkin models.

Isotherm model	Parameter	Parameter value
Langmuir	Q_L (mg.g⁻¹)	46.64
	b_L (L.mg⁻¹)	−1.55
	R²	0.682
Freundlich	K_F ((mg.g⁻¹)(mg.L⁻¹)^1/n)	27.09
	N	3.72
	R²	0.957
Temkin	K_T (L.mg⁻¹)	4.71
	b_T (J.mol⁻¹)	182.91
	R²	0.979

4 Conclusion

(Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs (0.00 ≤ x ≤ 0.04) were successively prepared by the co-precipitation method. Upon increasing the Ru³⁺ content, the percentage of the hematite phase decreased. This confirmed the successful incorporation of the dopants’ ions with the nanoferrites without forming secondary phases. The mean particle size of the prepared NPs ranged between 12 and 17 nm as revealed from the TEM analysis. Besides, the SEM analysis revealed the pseudo-spherical morphology of the NPs. The elemental composition of the ferrite nanoparticles was validated and the experimental atomic percentage of the elements was comparable with the theoretically calculated values. The direct and indirect bandgap energy and the Urbach energy of the synthesized ferrite NPs were in the range of 3.05–3.23 eV, 1.89–2.64 eV, and 0.99–0.73 eV, respectively. Among the prepared samples, the sample with x = 0.015 showed the highest adsorption rate and 60.73 % of CR dye was removed after 240 min. The adsorption rate of this sample was examined at different conditions, and it revealed that the best adsorption conditions were 80 mg of adsorbent amount, 308 K and a pH of 3.45. (Cd_0.4Ni_0.4Mn_0.2)Fe_2-xRu_xO₄ NPs with x = 0.015 preserved an improved adsorption activity after being reused for 4 cycles. Furthermore, different models were examined to discuss the adsorption isotherm and the experimental data were correlated with the Temkin isotherm model.

Acknowledgments

This work was performed in the Advanced Nanomaterials Research Lab and Specialized Materials Science Lab at Beirut Arab University in collaboration with Alexandria University.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Introduction

Experimental techniques

Synthesis of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs
Characterization of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs
Adsorption performance of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs

Results and discussion

XRD analysis
TEM, HRTEM and SAED analysis
SEM and EDX analysis
Surface area measurement
UV–vis analysis
Adsorption performance of (Cd0.4Ni0.4Mn0.2)Fe2-xRuxO4 NPs

Conclusion

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