Magnetic and Dielectric Properties of Cobalt and Zirconium Co-Doped Iron Oxide Nanoparticles via the Hydrothermal Synthesis Approach

Abstract

This study investigates the magnetic and dielectric properties of cobalt–zirconium co-doped iron oxide nanoparticles synthesized via the hydrothermal method. The synthesis was conducted at 150 °C, with reaction times of 4, 6, 8, 10, and 12 h. Co-doping with cobalt and zirconium significantly influenced the magnetic phase formation of iron oxide. Magnetic properties were characterized using a Vibrating Sample Magnetometer (VSM), revealing ferromagnetic behavior with a maximum saturation magnetization of 45 emu/g for the 8 h sample. The dielectric properties were analyzed through impedance spectroscopy across a wide frequency range, and the results were interpreted using Maxwell–Wagner’s model and Koop’s theory. The dielectric constant reached its maximum value of approximately 58 at a logarithmic frequency of 1.5 Hz for the sample synthesized for 8 h. This study highlights the importance of synthesis time in optimizing both the magnetic and dielectric properties of (Co, Zr) co-doped iron oxide nanoparticles.

1. Introduction

Nanoscience is fundamentally concerned with studying the structure and properties of materials on a tiny scale, explicitly focusing on the application of nanostructured materials. These materials possess at least one dimension that lies within the nanometer scale. New nanostructured materials have surprising magnetic, optical, electrical, and mechanical properties, and these nanostructured materials are being utilized in electronics, bioengineering, information technology (IT), and environmental applications. Nanomaterials are a crucial part of nanoscience and are vital in nanotechnology. They are essential because of their unique properties at a tiny scale, enabling new and valuable applications in various scientific areas [1]. Magnetite (Fe₃O₄) is categorized as a type of iron ore and exhibits a rock-like mineral appearance. As iron oxide, it demonstrates ferrimagnetic characteristics and is capable of acquiring permanent magnetism in a magnetic field. The magnetized form, recognized as lodestone, possesses a distinctive brownish-black or black coloration with an inherent luster. Its chemical configuration is represented as Fe²⁺Fe₂³⁺O₄²⁻, and its structural arrangement shows inverse spinel configuration [2]. Rare-earth-doped spinel ferrites are highly valued materials due to their unique chemical and physical properties, making them suitable for a wide range of scientific and industrial applications. These materials are utilized in areas such as permanent magnets, spintronics, high-density data storage, magnetic fluids, high-frequency devices, magnetic filters, drug delivery systems, and biomedical technologies. The magnetic properties of doped spinel ferrites are strongly influenced by factors like synthesis methods, particle size and shape, and chemical composition. Their nano-scale versions, with a high surface-to-volume ratio, exhibit distinct behaviors compared to their bulk counterparts, enhancing their applicability in advanced technologies. Spinel ferrites have a general formula of MFe₂O₄, where M represents divalent transition metal ions such as Mn²⁺, Co²⁺, Zn²⁺, Cu²⁺, or Ni²⁺, and their crystal structure comprises a face-centered cubic (FCC) arrangement formed by oxygen ions. The transition metal ions occupy tetrahedral (A site) or octahedral (B site) positions, and the distribution of these cations plays a critical role in determining the magnetic properties. This distribution, along with the doping concentration, type of dopant ions, and synthesis techniques, allows the precise tuning of the physical and chemical properties of nano-ferrites [3]. The application of magnetism and ferrites is spread across various fields, leading to vast research and discourse within the scientific community. Many ferrites have been synthesized and analyzed using different methods, including hydrothermal synthesis, sol–gel techniques, sputtering, spin coating, and other appropriate approaches for nanoparticle preparation. Researchers have focused on investigating magnetic, electrical, and optical properties through various methodological approaches, contributing to the enhanced properties of these materials [4].

The importance of the physical and chemical properties of multi-component inorganic nanostructured materials has inspired both technological and scientific interest. This interest urges the development of various devices, including those with magnetic, electric, catalytic, and spintronic functionalities [5]. Hydrothermal synthesis is an extensively used method for producing nanomaterials through solution-based reactions across several temperatures. The process enables control over material morphology and size by adjusting temperature and pressure conditions according to vapor pressure. This method offers advantages such as synthesizing unstable nanomaterials at higher temperatures and minimizing material loss for those with high vapor pressures.

Furthermore, hydrothermal synthesis allows precise control over nanomaterial structures through liquid or multiphase chemical reactions. Recent research within this domain has involved various nanomaterials, including nanoparticles, nanorods, nanotubes, hollow nanospheres, and graphene nanosheets. Novel synthesis techniques like microwave-assisted hydrothermal and template-free self-assembling catalytic synthesis have also emerged, alongside efforts to optimize synthesis conditions [6,7]. Ma et al. synthesized Co-doped Zn_1−xCo_xMn₂O nanocrystals that form hollow nanospheres by hydrothermal methods. Co-doping reduced crystalline size, narrowed the band gap, and enhanced photocatalytic activity, particularly in methyl orange degradation under visible light, suggesting the potential for pollutant remediation [8]. Monica et al. synthesized zirconia-doped hematite nanoparticles, xZrO₂-(1−x) α-Fe₂O₃ (x = 0.1 and 0.5) for gas-sensing applications [9]. Majid et al. synthesized Zn-doped nickel ferrites via the hydrothermal method, revealing structural changes with XRD and FTIR, increased saturation magnetization with higher Zn concentrations via VSM, and alterations in dielectric properties with impedance analysis, indicating the significant impact of Zn doping on nickel ferrite’s characteristics [10]. Hossain et al. synthesized Co-Zn ferrite via the sol–gel method, sintered at 900 °C. XRD confirmed the single-phase inverse spinel; SEM showed heterogeneous morphology; VSM revealed decreasing coercivity (36.08 Oe) with a higher Zn content; and dielectric analysis indicated Maxwell–Wagner polarization at room temperature [11]. Shifa et al. (2019) investigated Zr substitution effects on Co and Zn spinel properties via co-precipitation. They synthesized Co_0.5Zn_0.5Zr_xFe_2−xO₄ ferrites (x = 0.00–1.00), characterized by FTIR, XRD, and SEM. XRD revealed particle sizes (12–48 nm), and VSM confirmed single-phase spinel ferrites with coercivity (144.44 Oe) and remanence magnetization (13.72 emu/g) [12].

In this paper, the structural, magnetic, and dielectric properties of cobalt (Co) and zirconium (Zr) co-doped iron oxide (Fe₂O₃) nanoparticles, synthesized by the hydrothermal method, are illustrated. The nanoparticles show good ferromagnetic properties, which makes them promising candidates for magnetic storage devices to increase the memory of devices.

2. Experimental Set-up/Section

2.1. Materials

Hydrothermal synthesis was used to prepare cobalt–zirconium-iron oxide (CZIO) nanoparticles. The precursors CoCl₂·6H₂O, FeCl₂·4H₂O, ZrOCl₂·8H₂O, and distilled water were purchased from Sigma-Aldrich, located in St. Louis, USA and used without any further purification.

2.2. Preparation of Cobalt Zirconium Iron Oxide (CZIO) Nanoparticles

In the hydrothermal synthesis of co-doped CZIO nanoparticles, the precision balance was used to quantify different salts accurately. In total, 0.645 g of ZrOCl₂·8H₂O (pH 1), 0.2596 g of CoCl₂·6H₂O (pH 6), and 0.3976 g of FeCl₂·4H₂O (pH 2) salts, along with 20 mL of distilled water, were measured and placed in separate 100 mL beakers. The pH values of each solution were individually determined, reflecting the distinct pH levels associated with the different salts.

The salts were then thoroughly mixed in distilled water through a continuous stirring process facilitated by the inorganic nature of the salts. Combining the three solutions resulted in a final pH of 1. The prepared solution was tightly sealed into a Teflon-lined autoclave (sourced from Parr Instrument Company, located in Moline, USA) to generate high pressure and temperature. The autoclave was placed in a Box furnace (sourced from Carbolite Gero, located in Sheffield, UK) at 150 °C for varying durations, yielding five distinct samples with respective furnace times of 4, 6, 8, 10, and 12 h. Upon the completion of the hydrothermal treatment, each sample was allowed to cool and subsequently placed in centrifuge tubes for 2–3 h. The centrifugation separated the nanoparticles, causing them to settle at the bottom of the tube. The water was removed post-centrifugation, and the nanoparticle samples were retrieved using a spatula. The samples obtained were then dried in an oven, resulting in powdered-form nanoparticles.

This powdered form constituted the final sample, from which pellets were made as needed for further characterization. The detailed procedure ensured accuracy and reproducibility in synthesizing and characterizing cobalt–zirconium co-doped iron oxide nanoparticles. The schematic process is shown in Figure 1.

2.3. Characterization of Prepared Nanoparticles

In this study, a Lake Shore Vibrating Sample Magnetometer (VSM) (sourced from Lake Shore Cryotronics, located in Westerville, USA) and precision impedance analyzer (Wayne Kerr 6500B series) (sourced from Wayne Kerr Electronics, located in Bognor Regis, UK) were employed for the comprehensive characterization of magnetic and dielectric properties of the synthesized material. The experimental results spurted from the analytical tools are discussed in the following paragraphs, contributing to a thorough understanding of CZIO nanoparticle properties.

3. Results and Discussions

3.1. XRD Analysis

X-ray diffraction (XRD), governed by Bragg’s law nλ = 2d sinθ, is a powerful technique for determining the structural characteristics of materials by analyzing the scattering of X-rays at specific angles.

This method was applied to cobalt–zirconium co-doped iron oxide (CZIO) nanocrystalline samples with diffraction peaks, confirming the cubic spinel structure (Figure 2). The crystallite size (D) was calculated using the Debye–Scherrer formula: D = 0.9λ/βcosθ, where λ represents the X-ray wavelength (λ = 1.5406 Å), β represents the full width at half maximum peak value, and θ represents the peak position. The average crystallite size ranged from 16 to 24 nm. The XRD patterns of CZIO nanoparticles synthesized for durations of 4–12 h revealed spinel structure peaks indexed to planes. The doping of Co²⁺ and Zr⁴⁺ ions caused minor lattice distortions, reflected in slight peak shifts and systematic lattice parameter expansion. These results demonstrate the critical influence of synthesis duration on structural properties, yielding larger and more crystalline nanoparticles with potential applications requiring high structural integrity.

3.2. VSM Analysis

The magnetic properties of the nanoparticles were evaluated using a Vibrating Sample Magnetometer (VSM), a technique that measures the magnetization–hysteresis (M-H) loop, providing a detailed profile of the magnetic behavior of the material. The VSM results revealed that the saturation magnetization (Ms) of the cobalt- and zirconium-doped iron oxide (CZIO) nanoparticles was significantly enhanced compared to undoped iron oxide. The saturation magnetization values ranged between −30 and 45 emu/g, reflecting the material’s strong magnetic response to an applied external field. This enhanced magnetization was particularly evident in samples with an 8 h furnace time, highlighting the influence of synthesis conditions on the material’s magnetic properties.

The role of cobalt and zirconium in enhancing the saturation magnetization of iron oxide nanoparticles can be understood through both their direct and indirect effects on the magnetic structure and electron interactions. Cobalt doping significantly increases the saturation magnetization by introducing additional magnetic moments to the material. Cobalt ions (Co²⁺) have unpaired electrons in their d orbitals, which are responsible for generating localized magnetic moments. When cobalt is incorporated into the iron oxide lattice, these Co²⁺ ions replace some of the iron ions, disrupting the iron oxide’s original spin structure. The unpaired electrons in cobalt ions align with the external magnetic field, contributing directly to the total magnetization of the material.

The mechanism behind cobalt’s enhancement of saturation magnetization involves exchange interactions between the spins of cobalt and iron ions. Cobalt, with its larger atomic radius and distinct electronic configuration compared to iron, induces local distortions in the crystal lattice. These distortions affect the magnetic exchange interactions, promoting stronger spin alignment among the iron ions in the lattice. As a result, the combined magnetic moments of cobalt and iron ions lead to an increase in the overall magnetic moment of the nanoparticle, thereby enhancing saturation magnetization. Additionally, the introduction of cobalt can also modify the anisotropy of the material, leading to a more stable alignment of magnetic moments under an applied field, further contributing to the observed increase in magnetization.

Zirconium doping, while not contributing directly to the magnetic moment (since Zr⁴⁺ ions do not have unpaired electrons), plays a critical role in altering the structural and electronic properties of iron oxide, which, in turn, influences its magnetic behavior. Zirconium ions replace some of the iron ions or occupy interstitial sites within the lattice, causing distortions that affect the magnetic exchange interactions between iron ions. These distortions can alter spin ordering and magnetic coupling, enhancing the overall magnetization of the material. By modifying the electron density distribution in the lattice, zirconium doping influences the coupling between iron spins, potentially stabilizing the ferromagnetic phase and increasing the saturation magnetization.

The mechanism by which zirconium enhances the saturation magnetization is primarily through its effect on the lattice structure and spin interactions. The introduction of zirconium ions into the iron oxide lattice leads to changes in the iron ions’ local environment, which can affect the critical temperature for magnetic ordering (the Curie temperature) and increase the alignment of magnetic moments under an applied field. The disruption of the lattice symmetry caused by zirconium doping can lead to a more favorable arrangement of iron spins, which enhances the magnetic ordering and, consequently, the saturation magnetization. Moreover, the presence of zirconium could reduce magnetic anisotropy, allowing for the more efficient alignment of magnetic moments and improving the material’s overall magnetic response [13,14].

In the experimental context, CZIO, a ferromagnetic material, was the subject of investigation, as shown in Figure 3. Magnetic fields were systematically applied and studied for five prepared nanoparticle samples varying in their exposure time to the furnace at 4, 6, 8, 10, and 12 h. The collective analysis of these experiments produced a composite graph illustrating the saturation value, ranging between −30 and 45 emu/g. This result signifies the material’s magnetic saturation response due to the applied magnetic field, offering insights into its magnetic properties under different experimental conditions.

3.3. Dielectric Constant Measurements

Dielectric materials, which include electrical insulators, exhibit unique behavior when exposed to an electric field. Unlike conductors, where charges flow freely, dielectric materials undergo infinitesimal charge displacements, known as dielectric polarization, in response to the applied field. This behavior is influenced by the arrangement of the material’s molecules; in weakly bound systems, molecules tend to reorient in alignment with the electric field, enhancing polarization and, consequently, the dielectric response. These materials are essential in various applications, such as capacitors for energy storage, photocopying, and charge storage in laser printers. The dielectric properties of materials, particularly their energy dissipation and storage characteristics, have profound implications across numerous fields, including solid-state physics, electronics, optics, and biophysics [15]. The dielectric constant, ε ⃰, is expressed as follows:

ε ⃰ = ε′ − j ε″

(1)

where ε′, which is the real part of the dielectric constant, characterizes the energy stored within a material under the influence of an electric field. Conversely, ε″, the imaginary part of the dielectric constant, reflects the dissipated energy within the material. The determination of the real part of the dielectric constant (ε′) is facilitated through a prescribed mathematical formulation.

ε′ = CP t / A εₒ

(2)

Here, εₒ is the permittivity of free space (equal to 8.85 × 10⁻¹² F/m), t is the thickness of the pellet, A is the cross-sectional area of the pellet, and CP is the capacitance of the specimen expressed in Farad. The imaginary part of the dielectric constant (ε″) is calculated as follows:

ε″ = ε′ tanδ

(3)

where tanδ is the dielectric loss [16].

Figure 4 shows the observed fluctuations in the dielectric constant with the frequency at room temperature. The frequency was reported on a logarithmic scale. It was seen that the dielectric constant decreases with an increase in frequency, which is usual dielectric behavior [17]. The different characteristic curves can be explained through Koop’s theory based on the Maxwell–Wagner model [18,19]. These models provide a framework for understanding the role of interfaces between different phases or constituents in a material and their impact on dielectric properties. At low frequencies, the dielectric constant reaches a peak, which can be attributed to the ease with which dipoles within the material align with the applied electric field, enhancing polarization.

Maxwell–Wagner’s model posits that in heterogeneous materials, such as composites or materials with grain boundaries, the interfaces between phases significantly influence the dielectric behavior. At low frequencies, the dielectric constant is dominated by the polarization at these interfaces, where space charge accumulates and contributes to enhanced polarization. This leads to a higher dielectric constant. This is most pronounced at lower frequencies, such as at log 1.5 Hz, where dipoles can align with the electric field and maintain this alignment without being disrupted by the rapid alternations of the field.

The frequency-dependent dielectric permittivity described by Maxwell–Wagner’s model can be written as follows:

$$\mathsf{ϵ}(\mathsf{\omega })={\mathsf{ϵ}}_{\infty }+{\displaystyle \frac{\mathsf{\Delta }\mathsf{ϵ}}{1+\mathrm{j}\mathsf{\omega }\mathsf{\tau }}}$$

(4)

where “ϵ(ω)” is the frequency-dependent dielectric permittivity, “ϵ_∞” is the high-frequency permittivity, “Δ_ϵ” represents the dielectric contrast between the phases (e.g., between the grain and the grain boundary), “ω” is the angular frequency, and “τ” is the relaxation time. At low frequencies, charge carriers accumulate at the interfaces, resulting in enhanced polarization and a higher dielectric constant. However, as the frequency increases, the dipoles become unable to follow the changing electric field, reducing the effective polarization and leading to a decrease in the dielectric constant, as seen between log 2 and log 3 Hz in the experimental data.

Koop’s theory refines Maxwell–Wagner’s model by treating the grain boundaries as RC circuits, where each grain boundary is modeled as a capacitor in parallel with a resistor. This further contributes to the dielectric behavior of the material. At lower frequencies, the contribution from the grain boundaries is significant, as dipoles at these interfaces align with the applied field. However, at higher frequencies, the dipoles at the grain boundaries cannot respond quickly enough to the alternating field, and their contribution to the dielectric constant diminishes. The dielectric response at these higher frequencies is largely determined by the bulk material.

The frequency-dependent dielectric permittivity described by Koop’s model is given by the following equation:

$$\mathsf{ϵ}(\mathsf{\omega })={\mathsf{ϵ}}_{\infty }+{\displaystyle \frac{\mathsf{\Delta }\mathsf{ϵ}\mathrm{G}\mathrm{B}}{1+\mathrm{j}\mathsf{\omega }\mathsf{\tau }\mathrm{G}\mathrm{B}}}$$

(5)

where “ϵ_∞” is the high-frequency permittivity of the bulk material, “ΔϵGB” is the dielectric contrast at the grain boundaries, and “τGB” is the characteristic relaxation time associated with the grain boundaries. At low frequencies, the grain boundaries significantly contribute to the dielectric constant, but as the frequency increases, their influence diminishes, and the material’s dielectric response becomes dominated by the bulk.

In the case of cobalt- and zirconium-doped iron oxide, the dielectric constant exhibits a peak at approximately 58 at log 1.5 Hz, which is consistent with both Maxwell–Wagner’s model and Koop’s theory. At this frequency, the polarization at the interfaces is maximized, leading to an enhanced dielectric constant. As the frequency increases, the dipoles experience difficulty in realigning with the electric field, leading to a reduction in the dielectric constant between log 2 and log 3 Hz. This reduction is further exacerbated by imperfections within the material, such as defects and impurities, which hinder the mobility of dipoles.

Beyond log 4 Hz, the dielectric constant reaches a plateau, which indicates frequency-independent behavior. This plateau is characteristic of materials where the dielectric response is dominated by the intrinsic properties of the bulk material, and the influence of interfaces and grain boundaries becomes negligible.

3.4. Tangent Loss Measurements

The tangent loss or loss factor, tan δ, measures energy dissipation within the dielectric system. It describes how much electrical energy is utilized in different processes like electrical conduction, dielectric resonance, and the relaxation of materials. This loss occurs because of a delay between the electric field and the movement of charged particles. Therefore, the total dielectric loss is the sum of intrinsic and extrinsic losses. In perfect crystal, intrinsic losses depend primarily on the crystal structure and are caused by interactions between phonons and the electric field. Gurevich and Tagantsev explain this behavior [20]. Extrinsic losses are related to imperfections in the crystal, like boundaries between grains, small cracks, or other defects. These losses can be reduced with careful material processing. The amount of loss also depends on temperature and frequency. In a perfect material, intrinsic losses establish a theoretical limit for potential losses, whereas extrinsic losses are associated with imperfections or defects in the crystal structure [21].

Figure 5 illustrates distinct tangent loss curves, each describing the frequency-dependent energy dissipation behavior within the dielectric material. According to Koop’s theory, which furnishes a comprehensive framework for understanding dielectric phenomena, the tangent loss dynamics with frequency explain the energy dissipation mechanisms. Notably, the tangent loss rapidly increases at lower frequencies, revealing the maximum energy dissipation during dipole vibration and material damping [22]. A visible reduction in tangent loss was noticeable within the narrow frequency range of log 2 to log 3 Hz, and a constant trend was achieved beyond log 4 Hz, as expected. Structural defects, complex interfacial interactions, or inherent material flaws may interrupt the energy dissipation mechanism, leading to an unexpected increase in tangent loss within the specified frequency range of log 1.5 Hz.

3.5. Comparative Graph of Dielectric Constant and Tangent Loss

Figure 6a indicates the comparative results of the dielectric constant graph at a fixed frequency of log 1.5. In the first three samples from 4 to 8 h, there is a maximum increase in dielectric constant value that suggests that polarization is increased to approximately 58. After that, the dielectric constant value decreases. So, the 8 h sample is our optimal sample. Figure 6b indicates a tangent loss graph at a fixed frequency. At first, prepared nanoparticles do not lose much energy, but for 10 h and 12 h samples, tangent losses are very high due to complex interfacial interactions and structural defects in the crystals.

4. Conclusions

In conclusion, this study demonstrates the significant influence of cobalt and zirconium co-doping on the magnetic and dielectric properties of iron oxide nanoparticles synthesized via the hydrothermal method. By varying the synthesis time, we identified the optimal condition at 8 h, which resulted in the highest magnetic saturation of 45 emu/g and a peak dielectric constant of approximately 58. The analysis of dielectric properties, using Maxwell–Wagner’s model and Koop’s theory, revealed typical frequency-dependent behavior, confirming the importance of synthesis parameters in tailoring the material’s characteristics. The study underscores the role of synthesis duration in controlling the crystallization and magnetic coupling of the nanoparticles, enhancing their potential for applications in magnetic storage devices. These findings contribute to a deeper understanding of how co-doping and synthesis conditions can be optimized to improve the performance of iron oxide-based materials for advanced technological applications.