Synthesis of Iron Oxide Nanoparticles via Atmospheric Pressure Microplasma for High-Performance Energy Storage and Environmental Applications

Abstract

Energy and environmental challenges are driving researchers to explore cost-effective and eco-friendly nanomaterial fabrication methods. In this study, Atmospheric Pressure Microplasma (AMP) was used to synthesize iron oxide nanoparticles at varying molar concentrations of ferrous sulfate (0.5 M, 1 M, and 1.5 M) under a 15 kV discharge voltage for 90 min. The X-ray diffraction (XRD) results confirmed the formation of mixed cubic and hexagonal phases of magnetite and hematite nanoparticles. The particle size, calculated using the Debye–Scherrer formula, ranged from 9 to 11 nm, depending on the precursor concentration. Scanning electron microscopy (SEM) images revealed spherical nanoparticles at 0.5 M, while agglomeration occurred at 1.5 M. The energy-dispersive X-ray spectroscopy (EDS) analysis confirmed the presence of iron and oxygen in the synthesized nanoparticles. Fourier-transform infrared (FTIR) and UV spectroscopy showed characteristic absorption bands of iron oxide. The impact of the particle size and lattice strain on the optical properties of the nanoparticles was also studied. Smaller nanoparticles exhibited an excellent specific capacitance (627) and a strong charge–discharge performance in a 3 M KOH solution, with a high energy density (67.72) and power density (2227). As photocatalysts, the nanoparticles demonstrated a 97.5% and 96.8% degradation efficiency against methylene blue (MB) and methyl orange (MO), respectively, with high rate constants. These results surpass previous reports. The enhanced electrochemical performance and photocatalytic activity are attributed to the high-quality iron oxide nanoparticles, showing an excellent cyclic stability, making them promising for supercapacitors and environmental remediation.

1. Introduction

The rapid depletion of fossil fuels and the pressing issue of global warming have created an urgent need for renewable energy technologies to supplement and eventually replace traditional energy sources [1,2]. Challenges exist for renewable energy sources as their efficiency often depends on environmental factors. For instance, solar energy production is directly correlated with sunlight availability [3]. There is a growing demand for the development of effective energy storage devices. High-performance energy storage solutions are crucial for advancing next-generation consumer electronics, biomedical devices, and hybrid electric vehicles. In particular, the increasing demand for high power density has spurred research into electrochemical supercapacitors, which are established energy storage devices [4,5]. Supercapacitors have garnered significant research interest recently due to their high-power density, excellent reversibility, and longevity. These devices are widely used in various applications, including memory backup systems, electric vehicles, and other devices that require high-power pulses. Additionally, they enhance power delivery and extend the lifespan of primary power sources such as batteries. Supercapacitors are primarily classified into two categories based on their energy storage mechanisms: electric double-layer capacitors (EDLCs) and faradaic pseudocapacitors [6,7].

EDLCs typically utilize carbon-based materials, such as mesoporous carbon, activated carbon, CNTs, and graphene. Due to the rapid sorption and desorption of ions, carbonaceous supercapacitors generally exhibit high power capabilities and good conductivity. However, they tend to have low specific energy, as only the surface of the carbon is accessible for energy storage [8]. Pseudocapacitors utilize the pseudocapacitance properties of redox-active materials, such as conductive polymers and transition-metal oxides. They generally exhibit high energy density because they leverage the entire bulk of the material, not just the surface layer. Especially, various oxides of transition metals as well as hydroxides, such as such as RuO₂, MnO₂, NiO, Fe₂O₃, and so on, have been prominently used for supercapacitor applications due to their novel properties [9]. RuO₂ is the most promising material among these metal oxides as it could provide a very high charge storage capacity. However, RuO₂ has several limitations, including its high cost, toxicity, and scarcity [10]. Alternatively, the cost of MnO₂ is lower, and it has low toxicity along with a high specific capacitance. However, it suffers from poor cyclic stability issues [11]. As a result, researchers are actively exploring better alternatives for charge storage. One promising solution is to improve the charge storage capacity of low-cost, widely available, and environmentally friendly metal oxides. Specifically, the oxides and hydroxides of iron (Fe), nickel (Ni), and cobalt (Co) show encouraging properties, which make them strong candidates for use as electrode materials in supercapacitors [12].

Industrial wastewater contains significant amounts of toxic organic chemicals that are hazardous to human health and the aquatic environment. Several methods have been employed to remove these persistent harmful chemicals from water bodies, such as adsorption, flocculation, photocatalytic degradation, and chemical oxidation [13]. Metal oxide-based semiconductor photocatalysts have garnered significant interest due to their use in environmental remediation, especially in the photocatalytic degradation of pollutants in air and water [14]. These types of semiconductor photocatalysts have garnered significant attention as a simple, time- and energy-efficient technology that converts organic pollutants into eco-friendly, mineralized byproducts under solar irradiation. Out of several semiconductor materials, iron oxide nanoparticles, including Fe₃O₄, α-Fe₂O₃, etc., are recognized as among the most significant n-type semiconductors due to their unique properties and considerable scientific and technological importance [15]. Iron oxide NPs possess several fascinating characteristics that make them an attractive choice for a wide range of applications, including energy storage and photocatalysis. Given their diverse properties and extensive applications, different nanostructures of iron oxide, such as nanoparticles, nanowires, and nanotubes, have been widely synthesized using different methods [16].

Such types of iron oxide nanostructures have been synthesized using various non-plasmic methods like mechanochemical (i.e., laser ablation arc discharge, combustion, electrodeposition, and pyrolysis) and chemical (sol–gel synthesis, template-assisted synthesis, reverse micelle, hydrothermal, coprecipitation, etc.) methods [16]. Each method has particular challenges that need to be addressed in order to produce high-purity products [17]. Non-plasmic methods, for example, require long reaction times, consume significant amounts of energy, and operate in an inert atmosphere. These methods also involve multi-step processing and utilize expensive reactors. As a result, they often produce poor particle size distribution and generate liquid or chemical waste [18,19]. On the other hand, conventional plasmic methods operate under low pressures and high temperatures (above 1000 °C) and also rely on costly reactors, and, moreover, they typically produce a broader particle size distribution, which limits their industrial applicability. Among the various methods discussed, the microplasma synthesis technique stands out as a significant advancement in the fabrication of nanomaterials [20]. Its versatility is evident both in the manufacturing process and in its applications across numerous fields. Microplasma refers to plasma with dimensions ranging from tens to thousands of micrometers (10 µm to 1000 µm) or in the submillimeter range in at least one dimension. Microplasma boasts several key advantages compared to conventional plasma, due to its efficient surface-to-volume ratio and the decreased electrode spacing [21].

Apart from this, its microscale geometry, ability to operate at atmospheric pressure and room temperature, narrow particle size distribution, and the possibility of using compact, low-cost reactors also offer additional benefits. Additionally, electrons with a high density (∼${10}^{20} {\mathrm{m}}^{-3}$) and high energies (≥10 eV) involved in AMP can effectively interact with liquid or solid precursors to generate reactive radical species which facilitate the nucleation of nanostructures [22]. This process allows metal cations’ reduction in the aqueous phase, which enables a continued nanostructure synthesis due to the persistent flow rate of the plasma irradiation [23]. This Atmospheric Microplasma (AMP) technique has been reported to significantly outperform most of the chemical methods and other hydro/solvothermal methods. So far, numerous microplasma configurations and processes have been developed for producing different metal oxide nanoparticles, including iron oxide. The microplasma configuration is commonly used to prepare a wide range of metal oxide nanomaterials. This method is advantageous because it allows for the use of sacrificial metal electrodes or tube/external electrodes with flexible precursors. This flexibility facilitates various ways to connect the power supply necessary for igniting and sustaining the plasma. So far, a variety of nanoparticles have been synthesized using the Atmospheric Microplasma (AMP) technique. For example, ZnO nanostructures have been prepared using different ionic surfactants and non-ionic fructose through the microplasma technique [24]. However, few studies have reported on the preparation of iron oxide nanoparticles using the AMP technique [25].

A study of the characteristics of AMP-generated iron oxide nanoparticles revealed that a high energy density leads to a faster reaction rate. Additionally, SEM images indicated that AMP-generated iron oxide nanoparticles are less agglomerated compared to those produced using hydrothermal and other traditional synthesis methods [19]. Every phenomenon has its limitations; for microplasma, the main challenges are a low output and limited deposition areas. However, these issues can be effectively addressed using plasma arrays [26]. Therefore, in this study, ferrous sulfate was used as a precursor to fabricate iron oxide nanoparticles using a microplasma arc approach. The size of the nanoparticles was adjusted by utilizing precursor concentrations of 0.5 M, 1 M, and 1.5 M. These nanoparticles were analyzed through microscopic and spectroscopic techniques and evaluated for their effectiveness as electrode materials in supercapacitors. Additionally, the nanoparticles demonstrated excellent photocatalytic properties in degrading organic dyes, specifically methyl orange and methylene blue. The nanoparticles synthesized at a 0.5 M precursor concentration exhibited a high surface-to-volume ratio, more active sites, a strong cyclic stability, significant Faradaic redox reactions, and impressive degradation capabilities. To avoid slow, costly, and temperature-sensitive routes for nanoparticle fabrication, this microplasma synthesis method is a more reliable approach for producing highly controlled and versatile nanoparticles.

2. Results and Discussion

2.1. XRD Results

Figure 1a shows the XRD profile of the synthesized material, which points towards the formation of a mixed phase of iron oxide nanoparticles. The XRD pattern exhibited several diffraction peaks at 2$\mathsf{\theta }$ = ${35.8}^{\circ }$, ${43.4}^{\circ }$, ${62.8}^{\circ },$which represent the cubic phase of the materials indexed at (311), (400), and (440) of ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ (magnetite), respectively. Meanwhile, the diffraction peaks at 2$\mathsf{\theta }$ = ${24.4}^{\circ },{33.6}^{\circ }$, and ${37.2}^{\circ }$ show a rhombohedral (hexagonal) hematite ($\mathrm{\alpha }-{\mathrm{F}\mathrm{e}}_2{\mathrm{O}}_3)$ phase with index planes (012), (104), and (110). These patterns of XRD have been matched with standard patterns of the XRD of both ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ (magnetite) (refers to the JCPDS data # 87-2334) and ${\mathrm{\alpha }-\mathrm{F}\mathrm{e}}_2{\mathrm{O}}_3$ (hematite) (refers to the JCPDS data # 39-1346) [27,28].

The results indicate the formation of a multiphase nanostructure, as evidenced by the presence of crystalline phases of both magnetite and hematite in the XRD patterns. The X-ray diffraction shows prominent peaks along the (400) reflection plane, indicating a significant number of crystallites organized along this lattice direction. Notably, the 1.5 M precursor concentration produced sharper diffraction peaks compared to the 1 M and 0.5 M concentrations. This can be attributed to the smaller full width at half maximum (FWHM) of the iron oxide peaks at 1.5 M, suggesting the formation of larger crystallites. Additionally, the diffraction pattern of the iron oxide generated at 0.5 M exhibits more noise. The enhanced and well-defined intensity of the magnetite and hematite phases at higher concentrations is due to the increased crystallinity. This indicates that although variations in the concentration of ferrous sulfate do not alter the phases of iron oxide, they do influence the crystallinity of the samples. Specifically, the phase stability is independent of concentration changes, but crystallinity is dependent on the concentration percentage. Higher concentrations lead to the formation of larger, more crystalline materials. Furthermore, it is evident that the magnetite phases of iron oxide are more intense and better defined than the hematite phases, as supported by the existing literature.

The Gaussian mathematical fitting model was utilized to calculate the full width at half maximum (FWHM). A sample prepared at a molar ratio of 0.5 M, exhibiting a cubic structure with an FWHM of 0.92976, was analyzed. As the molar ratio increased to 1.5 M, the FWHM value decreased while the peak intensity increased, showing a characteristic peak at 2θ = [$43.3\circ$] with an FWHM of 0.7588. This indicates that with an increase in the concentration or the number of solute atoms, the FWHM value decreases while both the peak intensity and crystallinity increase. Scherrer’s formula, given in Equation (1), was employed to calculate the crystalline size

$$\mathrm{D}={\displaystyle \frac{\mathrm{k}\mathsf{\lambda }}{\mathsf{\beta }\cos \mathsf{\theta }}}$$

(1)

D represents the average crystalline size, K represents the crystalline shape factor which is taken 0.89 [29], $\mathsf{\lambda }$ represents the X-ray’s wavelength, $\mathsf{\beta }$ represents the line broadening in radians at the FWHM which is calculated by Guassian fitting of peaks, and $\mathsf{\theta }$ represents Bragg’s diffraction angle calculated in degrees [29]. In order to observe mechanical properties, like the strength and hardness of the material, the dislocation density $\mathsf{\delta }$ was calculated utilizing Equation (2) by measuring the crystalline size of the particle [30].

$$\mathsf{\delta }={\displaystyle \frac{1}{{\mathrm{D}}^2}}$$

(2)

The crystalline size is inversely related to both the full width at half maximum (FWHM) and the dislocation density. In the case of the sample fabricated at a molar ratio of 0.5 M, it exhibited a high dislocation density of 12.1 × ${10}^{-3}$ ${\mathrm{n}\mathrm{m}}^{-2}$ and a small crystalline size of 9.09 nm. In contrast, the sample fabricated at a molar ratio of 1.5 M displayed a lower dislocation density of 8.05 × ${10}^{-3}$ ${\mathrm{n}\mathrm{m}}^{-2}$ and a larger crystalline size of 11.14 nm, as shown in Figure 1b. This difference may be attributed to the presence of more iron cations, which tend to cluster together and increase the growth rate at the nucleation sites. Both the size of the crystal and its dislocation density are indicators of the material’s softness and hardness. The findings suggest that lower precursor concentrations lead to higher dislocation densities, which in turn enhances the material’s strength, making it harder and stronger, while reducing its ductility. These observations align with the Bailey–Hirsch relation [31], which indicates that materials with a greater dislocation density exhibit a higher yield strength. Since magnetite has a simple cubic structure (with a = b = c), the lattice parameter a (${\mathrm{A}}^{\circ }$) can be calculated using Equation (3) [32].

$$\mathrm{a}=\sqrt{{\displaystyle \frac{{\mathsf{\lambda }}^2}{4{\mathrm{s}\mathrm{i}\mathrm{n}}^2\mathsf{\theta }/\mathrm{s}}}}$$

(3)

Table 1. Calculation of lattice parameters of iron oxide.

Molar Ratios	$\mathbf{a} ({\mathbf{A}}^{\circ }$)
0.5 M	8.32
1 M	8.33
1.5 M	8.34

represents the calculations of the lattice parameters evaluated from the XRD data, which shows that the lattice parameters of the sample prepared at a 0.5 M concentration is 8.32 ${\mathrm{A}}^{\circ }$ which increases to 8.34 ${\mathrm{A}}^{\circ }$. With increased molar concentrations, the lattice parameter values increase due to the slight shifting of peaks towards lower angles (Figure 1a).

2.2. SEM

Figure 2 shows the SEM images of the material synthesized at various concentrations. From Figure 2a, the morphology of the synthesized nanoparticles appears spherical. However, as the concentration of solute atoms increases, these particles gradually transform into larger nano-domains due to the agglomeration at nucleation sites, as shown in Figure 2b. Figure 2c illustrates the final morphology of the nanoparticles at the maximum solute concentration, revealing clusters of flower-shaped crystallites that feature larger grain boundaries. This change leads to a reduction in both the surface area and surface free energy. The average size of iron oxide nanoparticles at a concentration of 0.5 M, as observed from these nano-domains, was approximately 9 nm, 21 nm, and 40 nm, which are in agreement with the literature [25]. At higher concentrations, the crystalline size increases, leading to the formation of agglomerates that can exceed 200 nm in diameter [33]. These findings are consistent with earlier research on the synthesis of iron oxide nanoparticles through oxidative alkaline hydrolysis, where agglomeration occurred at elevated concentrations of ferrous sulfate due to overlapping grains, resulting in clusters of nanoparticles. Similarly, silver nanoparticles synthesized using atmospheric pressure microplasma also exhibited coalescence at higher precursor concentrations, leading to larger particle diameters [34,35].

Agglomeration occurs due to the high surface energies and surface areas of nanoparticles, which make the atoms on the surface unstable. To stabilize themselves, these surface atoms begin to agglomerate by forming bonds with adjacent surface atoms. Additionally, agglomeration can be influenced by weaker electrostatic forces, such as Van der Waals forces, between atoms. These agglomerates can also exhibit magnetic dipole–dipole interactions between isolated iron nanoparticles. Changes in the concentration of precursor atoms can alter grain shapes, sizes, and surface effects, resulting from the rearrangement of atoms at different nucleation sites. The surface characteristics significantly impact the magnetic properties of iron oxide nanoparticles. Furthermore, the morphology of these nanoparticles can be modified by adjusting the concentrations of precursors [36]. Additionally, the elemental composition of the sample was determined using the energy-dispersive X-ray spectroscopy (EDX) of iron oxide nanoparticles at a molar ratio of 0.5 M. The EDX peak for iron (Fe) was observed at 0.8 KeV, while oxygen (O) appeared at 0.5 KeV, and sulfur (S) at 2.3 KeV. The EDX spectra revealed significant signals for the main elements of iron and oxygen, which were found to be 51.38 wt% and 32.31 wt%, respectively, as shown in Figure 2d. These data confirm the successful synthesis of iron oxide nanoparticles [37].

2.3. FTIR Results

Figure 3 exhibits the FTIR spectra of iron oxide nanoparticles at various molar concentrations. The spectrum is recorded in the mid-infrared range (400–4000 cm⁻¹). After being oxidized in distilled water, ferrous sulfate dissociates into iron and sulfate ions, which then undergo different chemical reactions to form various compounds. In Figure 3, the absorption bands at 806, 1010, 1071, and 1485 cm⁻¹ correspond to the presence of ferrous sulfate [38]. The absorption bands at 1629 cm⁻¹ and 658 cm⁻¹ indicate the existence of magnetite and the Fe-O stretching mode typical of hematite, respectively [39,40]. The absorption spectra observed at 2320 cm⁻¹ confirm the formation of iron nanoparticles, which exhibit absorption peaks at a wavelength of 2352 cm⁻¹. This shift in the peaks of the nanoparticles may be attributed to the size of the nanoparticles and the composition of the surrounding media [41]. Additionally, the broadband in the range of 3100–3450 cm⁻¹ results from hydrogen bonding interactions or –OH stretching [42]. Another absorption band is observed at 1071 cm⁻¹, while a weaker band is noted at 1485 cm⁻¹ [38]. Higher intensity peaks at the absorption band of 1629 cm⁻¹ indicate that the oxidation rate increases with higher precursor concentrations, leading to the formation of iron oxide nanoparticles [39,40].

Furthermore, the stretching of the OH group occurs due to hydrogen bonding [42]. In this case, the band becomes less broad and sharper at higher concentrations, suggesting a reduction in hydrogen bonding. Overall, the FTIR results demonstrate that ferrous sulfate reduces to iron oxide nanoparticles, with the bands related to iron oxide becoming sharper than those associated with iron. This observation indicates that the microplasma synthesis route is more effective for converting metal salts into metal oxide nanoparticles.

2.4. Optical Results

Figure 4 demonstrates the UV–visible absorption spectrum of iron oxide at various molar concentrations. Iron oxide nanoparticles demonstrate higher absorption rates at increased precursor concentrations, with maximum absorption peaks observed at 430 nm, 442 nm, and 450 nm for molar ratios of 0.5 M, 1 M, and 1.5 M, respectively. According to Mie scattering theory, larger particles tend to absorb higher wavelengths of incident light [43]. Therefore, the maximum absorption peak occurs at a molar ratio of 1.5 M, where larger-sized particles are synthesized, a finding that is further supported by the XRD and SEM results mentioned earlier. The UV–visible spectrum is also used to calculate the band gap using the Tauc relation given in Equation (4) [44].

$${\left(\mathrm{\alpha }\mathrm{h}\mathrm{ѵ}\right)}^{\mathrm{n}}=\mathrm{B}(\mathrm{h}\mathrm{\upsilon }-{\mathrm{E}}_{\mathrm{g}})$$

(4)

where α = the absorption coefficient and B = the constant, which shows a distinct value for each type of transition. ${\mathrm{E}}_{\mathrm{g}}$ = the energy band gap, $\mathrm{h}\mathrm{ѵ}$ = the incident photon’s energy, and n = the exponent having a value of 2 for the allowed direct transition and ½ for the allowed indirect transition. An absorption coefficient α can be calculated by Beer–Lambert’s relation, given as follows [44]:

$$\mathrm{\alpha }=2.303 {\displaystyle \frac{\mathrm{A}}{\mathrm{d}}}$$

(5)

where A is the absorbance obtained from UV spectra, while d is called the cuvette path length. The allowed direct band gap value ${\mathrm{E}}_{\mathrm{g}\left(\mathrm{d}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\right)}$ as well as the allowed indirect band gap value ${\mathrm{E}}_{\mathrm{g}\left(\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\right)}$ was calculated by extrapolating the straight line portion of ${\left(\mathrm{\alpha }\mathrm{h}\mathrm{ѵ}\right)}^2$ and ${\left(\mathrm{\alpha }\mathrm{h}\mathrm{ѵ}\right)}^{1/2}$ versus the $\mathrm{h}\mathrm{ѵ}$ graph to $\mathrm{h}\mathrm{ѵ}$ axis, as shown in Figure 5.

The Tauc plots for synthesized iron oxide nanoparticles with various compositions, representing both direct and indirect transitions, are illustrated in Figure 5a,b. The corresponding band gap values for these nanoparticles are summarized in

Table 2. Optical parameters of iron oxide.

Optical Parameters	${\mathbf{F}\mathbf{e}}_3{\mathbf{O}}_4$
	0.5 M	1 M	1.5 M
${\mathbf{E}}_{\mathbf{g}\left(\mathbf{d}\mathbf{i}\mathbf{r}\mathbf{e}\mathbf{c}\mathbf{t}\right)}$(eV)	2.88	2.8	2.75
${\mathbf{E}}_{\mathbf{g}\left(\mathbf{i}\mathbf{n}\mathbf{d}\mathbf{i}\mathbf{r}\mathbf{e}\mathbf{c}\mathbf{t}\right)}$(eV)	1.97	1.93	1.85
${\mathbf{E}}_{\mathbf{u}}$(eV)	0.78	1.27	1.47
n	1.54	2.33	2.63
k	$1.73 \times {10}^{-6}$	$3.76 \times {10}^{-6}$	$5.85 \times {10}^{-6}$
${\mathbf{Ԑ}}_{\mathbf{r}}$	2.39	5.45	6.94
${\mathbf{Ԑ}}_{\mathbf{i}}$	$0.54 \times {10}^{-5}$	$1.76 \times {10}^{-5}$	$3.09 \times {10}^{-5}$
tanθ	0.22	0.32	0.44
σ	$0.2 \times {10}^{10}$	$0.7 \times {10}^{10}$	$1.3 \times {10}^{10}$
R%	15.7%	31%	43.9%

. It is noteworthy that the energy band gap values of the iron oxide nanoparticles exceed those of the bulk iron oxide. This increase in the band gap can be attributed to the small size of the nanoparticles. In these structures, the reduced atomic dimensions lead to less overlap of the energy levels, resulting in a wider band gap, as the energy difference between the conduction and valence bands increases. In contrast, bulk materials have larger atomic dimensions that allow for a greater overlap of energy levels. This increase in overlap leads to a broader bandwidth and a decrease in the energy gap between the valence and conduction bands. Consequently, a material that demonstrates metallic properties in its bulk form may experience a change in its electronic properties when it is reduced to a nanostructure [45]. In addition, the large band gap values of nanoparticles may be due to quantum confinement. The energy band gap values are close to the literature. The variation in the band gap values is due to differences in the size of nanoparticles for different precursor’s concentrations. Figure 5a,b show the decrease in the energy band gap with an increasing molar concentration. A higher precursor concentration leads to an increase in the particle size of the fabricated nanoparticles, as supported by the XRD and SEM results. This increase in particle size is responsible for the observed decrease in the band gap values as the energy band gap of nanoparticles has an inverse relation with their sizes [46]. Also, the direct band gap values are larger than indirect band gap values. Both values of the direct and indirect energy band gap of iron oxide (magnetite) have classified this sample as a semiconductor, because the energy band gap of the semiconductor lies in the range of 0–3 eV [47].

In addition, the defect bands that are formed within the band gap of iron oxide nanoparticles as an intermediate state can also be determined. Due to these defects, the band tail is extended past the conduction minimum to the valence band maximum. This defect tail is called the Urbach tail, whereas the energy associated with this tail is referred to as Urbach energy. Urbach energy is given by Equation (6) [48].

$$\mathrm{\alpha }={\mathrm{\alpha }}_{\mathrm{o}}\mathrm{e}\mathrm{x}\mathrm{p}\left({\displaystyle \frac{\mathrm{E}}{{\mathrm{E}}_{\mathrm{U}}}}\right)$$

(6)

where $\mathrm{\alpha }$ = absorption coefficient, E = energy of photon, and ${\mathrm{E}}_{\mathrm{U}}$ = Urbach energy. The Urbach energy of iron oxide nanoparticles has been calculated for its various molar ratios by plotting a logarithm of the absorption coefficient (ln $\mathrm{\alpha }$) vs. the photon energy (h$\mathrm{\upsilon }$), as shown in Figure 5c. ${\mathrm{E}}_{\mathrm{U}}$ is calculated by taking reciprocals of the slopes of linear portions below optical band gap values. The Urbach energy is 0.78 eV, 1.27 eV, and 1.47 eV for 0.5 M/${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$, 1 M/${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$, and 1.5 M/${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$, respectively. Figure 5d shows how the value of the defect energy increases with the decrease in the value of the band gap. Also, the values of the Urbach energy of ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ samples are larger than in the literature [49]. The difference in these values is due to particle sizes, which also reflects the high conductivity of the sample.

The values of the refractive index and extinction coefficient of iron oxide nanoparticles are shown in Figure 6. The real part of the refractive index determines how fast light can pass through nanoparticles, which shows the degree of the transparency of that material. Whereas the imaginary part of the refractive index or extinction coefficient determines the attenuation of light due to absorption. Their values have been calculated from the following relationships:

$$\mathrm{n}={\displaystyle \frac{1}{{\mathrm{T}}_{\mathrm{s}}}}-\sqrt{{\displaystyle \frac{1}{{\mathrm{T}}_{\mathrm{s}}-1}}}$$

(7)

where ${\mathrm{T}}_{\mathrm{s}}$ = percent transmittance and ${\mathrm{T}}_{\mathrm{s}}$ = ${10}^{-A}$ × 100 [50]. Here, A = the absorption of light by the material.

The extinction coefficient has a linear relationship with the absorption coefficient, as follows [51]:

$$\mathrm{K}={\displaystyle \frac{\mathrm{\alpha }\mathsf{\lambda }}{4\mathsf{\pi }}}$$

(8)

$\mathrm{\alpha }$ = the absorption coefficient and $\mathsf{\lambda }$ = the wavelength of the incident photon.

The values of the real and imaginary parts of the refractive index increase with higher precursor concentrations due to the enhanced refraction and greater light attenuation associated with larger nanoparticles. Table 2 presents the values for the energy band gaps (both direct and indirect), Urbach energy, complex refractive index, complex dielectric constant, dissipation factor, optical conductivity, and reflectance percentage. These values are consistent with those found in the literature [51]. In summary, variations in the particle size and lattice strain significantly affect the optical properties of nanoparticles. Additionally, the symmetry of the lattice plays a role in influencing these optical characteristics. Given its cubic symmetry, the material has an optically isotropic crystalline structure, meaning that nanoparticles exhibit the same values of optical constants in all directions. Consequently, the direction of the light beam’s vibration does not alter the values of the refractive index or dielectric constant.

The dielectric constant of iron oxide nanoparticles has also been depicted in Figure 7, which has been calculated from the relationship between the dielectric constant and refractive index. The real part of the dielectric constant is given as follows [51]

$${\mathrm{Ԑ}}_{\mathrm{r}}={\mathrm{n}}^2-{\mathrm{k}}^2$$

(9)

Here, n = the real part of the refractive index and k = the imaginary part of the refractive index given in the above equations.

The imaginary part of the dielectric constant is given by Equation (10) [51]

$${\mathrm{Ԑ}}_{\mathrm{i}}=2\mathrm{n}\mathrm{k}$$

(10)

Whereas the dielectric constant’s dissipation factor of the nanoparticles can also be calculated through the following equation [51]

$$\mathrm{t}\mathrm{a}\mathrm{n}\mathsf{\theta }={\displaystyle \frac{{\mathrm{Ԑ}}_{\mathrm{i}}}{{\mathrm{Ԑ}}_{\mathrm{r}}}}$$

(11)

A fundamental aspect of the dielectric constant is its role in determining the degree of polarization of a material, while the imaginary part indicates dielectric loss. Both components of the dielectric constant exhibit similar trends as the refractive index. The value of the dielectric constant varies across the wavelength of a photon due to changes in polarization that are influenced by the valence states of cations ${\mathbf{F}\mathbf{e}}^{2+}$ and the polarization of space charges. The crystalline size and lattice strain significantly affect the formation of dipoles and their polarization within nanoparticle structures. The dissipation factor indicates the amount of heat generated in dielectric materials. This factor is particularly important for all samples at low wavelengths and with high-energy photons, suggesting that nanoparticles release more energy as heat with the increasing photon energy [52].

The optical conductivity and reflectance of ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ nanoparticles have also been illustrated in Figure 8. It can be seen that both parameters show the maximum behavior at a low wavelength and with high-energy photons [51]. Also, both parameters have maximum values for the higher precursor’s concentration. The high optical conductivity suggests that the optical excitation of electrons within ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/1.5 M is greater than in ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/0.5 M. It might be due to the large concentration of charge carriers that appear within ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/1.5 M due to the large-sized nanoparticles [53].

2.5. Photocatalytic Activity

2.5.1. Analysis of Photocatalytic Activity

Figure 9 and Figure 10 illustrate the degradation of methylene blue and methyl orange using iron oxide nanoparticles under UV-Vis light. Nanoparticles (NPs) synthesized with precursor concentrations of 0.5 M, 1 M, and 1.5 M were employed in the degradation process. Notably, the NPs synthesized at a 0.5 M precursor concentration demonstrated the highest degradation rates for both methylene blue and methyl orange when compared to the ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ NPs synthesized at 1 M and 1.5 M, as shown in Figure 9a–c, as well as Figure 10a–c. The small size of the nanoparticles provides a larger surface area for adsorption, resulting in an increased number of active sites that interact strongly with dye molecules [54]. The observed improvement in the catalytic response is attributed to a reduction in defect sites within the photocatalyst. This reduction helps to inhibit the recombination of electron–hole pairs, thereby promoting the formation of radicals. Additionally, the enhanced contact between the photogenerated radical species and the catalyst surface facilitates more efficient interactions, which ultimately leads to an accelerated decomposition rate. Figure 9d and Figure 10d show maximum percentage degradations of methylene blue (97.5%) and methyl orange (96.8%) by ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ nanoparticles, respectively. The degradation efficiency of methylene blue (MB) is greater than that of methyl orange (MO) [55] under similar conditions due to the following reasons:

• Molecular Structure Stability: The two dyes differ in the stability of their molecular structures, as illustrated in Figure 11. Methylene blue (MB) shows a greater electron delocalization across larger carbon atoms when compared to the single benzene rings found in methyl orange (MO). This increased delocalization weakens the conjugated single and double bonds in MB, making it easier to remove π-electrons compared to MO.
• Energy Level Alignment: The relative positions of the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) of the dyes, in comparison to the conduction band (CB) and valence band (VB) edge potentials of the ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ NPs, are illustrated in Figure 12. The direction of the potential gradient is indicated by the arrow, pointing from positive to negative values. The CB and VB edge potentials for methylene blue (MB) and methyl orange (MO), as outlined in

  Table 3. CB-VB energy potential levels$({\mathbf{E}}_{\mathbf{C}\mathbf{B}},{\mathbf{E}}_{\mathbf{V}\mathbf{B}}$) and energy gaps (${\mathbf{E}}_{\mathbf{g}})$ of MB, MO dyes, and ${\mathbf{F}\mathbf{e}}_3{\mathbf{O}}_4$ NPs.

  Dye/Oxide	${\mathsf{\lambda }}_{\mathbf{m}\mathbf{a}\mathbf{x}}$ (nm)	${\mathbf{E}}_{\mathbf{g}}$ (eV)	${\mathbf{E}}_{\mathbf{C}\mathbf{B}}$ (eV)	${\mathbf{E}}_{\mathbf{V}\mathbf{B}}$ (eV)	Ref.
  MB	664	1.86	−0.25	1.61	[55]
  MO	464	2.01	−1.87	0.14	[55]
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/0.5 M	-	2.88	−0.18	2.7	Present work
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/1 M	-	2.8	−0.14	2.66	Present work
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/1.5 M	-	2.75	−0.115	2.635	Present work

  , were obtained from the existing literature, while those for the nanoparticles were calculated using standard equations for CB and VB potentials. In the case of MB, electrons transfer more readily from its LUMO to the CB of the catalyst compared to MO, due to the more positive LUMO levels in MB. The CB edge potential for MB is higher than that of MO, and its VB edge potential is significantly lower than that of MO. This alignment promotes rapid redox reactions, generating reactive radicals and enhancing the decomposition rate of MB. The degradation rates for MB and MO achieved in this study are higher than those reported in the literature, as presented in

  Table 4. Comparison of photocatalytic activity of iron oxide nanoparticles against methylene blue in accordance with literature.

  Catalyst	Concentration of MB Dye (grams)	Time of Degradation (min)	% Value of Photo Degradation	Reference
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	0.02	90	15%	[56]
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	0.05	60	77.7%	[57]
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	0.01	180	86%	[58]
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	0.02	360	78%	[59]
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	0.03	80	97.5%	Present study

  and

  Table 5. Comparison of photocatalytic activity of iron oxide nanoparticles against methyl orange in accordance with literature.

  Catalyst	Concentration of MO Dye (grams)	Time of Degradation (min)	% Value of Photo Degradation	Reference
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	0.005	60	58%	[60]
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	0.01	40	70%	[61]
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	0.04	210	81%	[61]
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	0.02	60	23%	[62]
  ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	0.03	80	96.8%	Present Study

  . These findings demonstrate that ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ NPs serve as highly effective photocatalysts, making them suitable for wastewater treatment to remove pollutants such as MB and MO.

2.5.2. Kinetic Study

In order to understand the kinetic behavior of iron oxide nanoparticles, plots for the $ln (C_0/C_t)$ vs. the irradiation time for both MB (Figure 11a) and MO (Figure 11b) have been presented. The linear fitting graphs corroborate that the dye degradation obeys the pseudo-first-order kinetics effect expressed by the Langmuir–Hinshelwood equation [63].

$$\mathrm{l}\mathrm{n}{\displaystyle \frac{{\mathrm{C}}_0}{{\mathrm{C}}_{\mathrm{t}}}}=\mathrm{k}\mathrm{t}$$

(12)

where ${\mathrm{C}}_0=$ the dye’s initial concentration, ${\mathrm{C}}_{\mathrm{t}}=$ the dye’s concentration after irradiation, $\mathrm{k}=$ the pseudo-first-order rate constant, and t = time

The regression correlation factor (R²) has been calculated from the graphs, along with the rate constant (K), which is derived from the slope of these graphs. The results are presented in

Table 6. Degradation percentage rate and pseudo-first-order rate constant for photocatalytic degradation of MB by iron oxide nanoparticles.

Samples	Degradation Efficiency (%)	Degradation Rate Constant K (min−1)	Half Life ${\mathbf{t}}_{1/2}$ (min)	${\mathbf{R}}^2$
${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/0.5 M	97.5	0.04503	15	0.98813
${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/1 M	93.5	0.03628	19	0.9868
${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/1.5 M	91	0.02986	23	0.94083

and

Table 7. Percentage degradation rate and pseudo-first-order rate constant for photocatalytic degradation of MO by iron oxide nanoparticles.

Samples	Degradation Efficiency (%)	Degradation Rate Constant (min−1)	Half Life ${\mathbf{t}}_{1/2}$ (min)	${\mathbf{R}}^2$
${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/0.5 M	96.8	0.04372	16	0.98773
${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/1 M	93.1	0.03406	20	0.97836
${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/1.5 M	90.2	0.02933	24	0.93423

which confirm a good fit for the model and indicate a rapid degradation rate for the dyes. Notably, the degradation of MB using a ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/0.5 M solution has the shortest half-life of 15 min, compared to a half-life of 23 min for the degradation of the same dye using a ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$/0.5 M solution.

$${\mathrm{t}}_{1/2}=\ln {\displaystyle \frac{2}{\mathrm{k}}}=0.693/\mathrm{k}$$

(13)

is the time taken for the concentration of the dye to reduce to its half value.

2.5.3. Mechanism of Dye Degradation

Figure 13 and Equations (14) and (15) illustrate the degradation mechanisms of MB and MO. Initially, light is absorbed by a photocatalyst. When exposed to light, electrons in the valence band are excited into the conduction band, creating an excess of electrons in the conduction band and generating holes in the valence band. These electron–hole pairs tend to recombine, but they can also transfer to the surface of the photocatalyst to participate in various reactions. In the case of strong photocatalysts, electrons and holes undergo photodegradation, as demonstrated in this research. For instance, iron oxide nanoparticles have shown a maximum photocatalytic degradation rate of 97.5% against MB and 96.8% against MO. Electrons participate in reduction reactions, while holes engage in oxidation reactions. The photogenerated electrons from the conduction band can react with O₂ molecules (either present or absorbed on the photocatalysts surface or nearby), producing superoxide anions (•O²⁻) or convert H₂O₂ into hydroperoxyl radicals (HOO•), as shown in Equations (16) and (17). Similarly, the holes in the valence band can oxidize or produce hydroxyl radicals (•OH), as indicated in Equations (18)–(20). These superoxide anions and hydroxyl radicals are highly reactive and can participate in various chemical reactions. Additionally, dyes can also absorb light, causing electrons to transition from the HOMO to the LUMO, which can contribute to the electron injection into the conduction band. Ultimately, the reactive radicals produced can degrade pollutant molecules into smaller, less harmful substances, leading to the purification of water and the atmosphere.

$${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4+h\upsilon ⭢{Fe}_3{O}_4({\mathrm{e}}^{-}+{\mathrm{h}}^{+})$$

(14)

$${\mathrm{H}}_2{\mathrm{O}}_2+h\upsilon \to 2(•OH)$$

(15)

$$e^{-}+O_2\to •O^{2-}$$

(16)

$${\mathrm{e}}^{-}+H_2{O}_2\to •HOO+H^{+}$$

(17)

$$h^{+}+H_{2}O\to •OH+H^{+}$$

(18)

$${\mathrm{h}}^{+}+{\mathrm{H}}_2{\mathrm{O}}_2\to •\mathrm{OH}+{\mathrm{O}\mathrm{H}}^{-}$$

(19)

$$h^{+}+{OH}^{-}\to •OH$$

(20)

$$MB+h\upsilon ⭢{MB}^{+}\to {MB}^{+}+e^{-}$$

(21)

$$MO+h\upsilon ⭢{MO}^{+}\to {MO}^{+}+e^{-}$$

(22)

$$MB+{MB}^{+}+•OH\to Degraded products$$

(23)

$$MO+{MO}^{+}+•OH\to Degraded products$$

(24)

2.6. Electrochemical Results

2.6.1. Cyclic Voltammetric Results

In Figure 14 various voltammograms of iron oxide nanoparticles are presented alongside their corresponding capacitance bar graphs. Figure 14a displays the cyclic voltammograms of iron oxide nanoparticles at different molar concentrations with a scan rate of 5 mV/s. The specific capacitance was determined by calculating the area under the curve, yielding values of 420 $\mathrm{F}/\mathrm{g}$, 558 $\mathrm{F}/\mathrm{g}$, and 627 $\mathrm{F}/\mathrm{g}$, for ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4/1.5\mathrm{M},{\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4/1\mathrm{M}$, and ${\displaystyle \frac{{\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4}{0.5}}\mathrm{M},\mathrm{a}\mathrm{s}$ indicated in Figure 14b. The shapes of the cyclic voltammograms provide insight into the behavior of the electrode material and its characteristics as a capacitor for energy storage. Ideally, the voltammograms of capacitors exhibit a rectangular shape; however, metal oxide electrodes display a pseudo-capacitive nature due to Faradaic reactions [64]. Among various pseudocapacitor materials, iron oxides are highly favored due to their low cost, low toxicity, environmental friendliness, and natural abundance. The reversible redox curves further confirm the formation of iron oxide [65]. Additionally, the charge storage mechanism in pseudocapacitors occurs through either redox reactions or the physisorption–desorption method [66]. Since the dissociation rate of KOH is slow, the redox reaction between KOH and ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$at the electrode–electrolyte interface causes a phase transition in the electrode material, enabling pseudocapacitance. Notably, the redox reaction is not limited to the surface of the electrode; it also occurs within the bulk of the material. Therefore, KOH results in a significant enhancement in specific capacitance. The possible reactions at the electrode in KOH are outlined below:

The redox reactions between the $\mathrm{F}\mathrm{e}₃\mathrm{O}$₄ and OH⁻ ions are reversible, as indicated by the symmetric behavior of the CV curves. The impressive capacitive performance of the iron oxide electrode, prepared with a 0.5 M precursor concentration, can be attributed to the small crystalline size of the NPs. This smaller size facilitates the formation of a well-ordered and stable structure, which is advantageous for the electrode performance. Furthermore, the reduced agglomeration and smaller crystalline structure promote a nanostructure that enhances the ion diffusion within the material. Additionally, the structural stability of the electrode material improves with smaller crystalline sizes, leading to a better electrochemical performance even under harsh conditions or prolonged cycling [67]. The enhanced specific capacitance values are displayed in

Table 8. Comparison of specific capacitance of magnetite-based electrodes.

Sr.No	Specimen	Morphology	Synthesis Route	Scan Rate/Current Density	Specific Capacitance F/g	Ref.
1	$\mathrm{PVP}-\mathrm{coated}{\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	Nano spheres	Solvothermal method	$2 \mathrm{mV}{\mathrm{s}}^{-1}$	396	[69]
2	Thin film ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	Nano grains	SILAR deposition	$1 \mathrm{mA}{\mathrm{c}\mathrm{m}}^{-2}$	487.84	[70]
3	$\mathrm{Pure} {\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	Nanowires	Hydrothermal method	$2 {\mathrm{mVs}}^{-1}$	48	[71]
4	$\mathrm{Pure} {\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	Nano films	Co-precipitation method	$0.4 \mathrm{mA} {\mathrm{c}\mathrm{m}}^{-2}$	82	[72]
5	$\mathrm{Pure} {\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	Nano spheres	Electrodeposition technique	$2 {\mathrm{mVs}}^{-1}$	201.3	[73]
6	$\mathrm{Graphite}-\mathrm{supported}{\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	Monolayered structure	Thermal decomposition	$0.5 \mathrm{A}{\mathrm{g}}^{-1}$	300	[74]
7	$\mathrm{Pure} {\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$	Nano spheres	Microplasma technology	$5 {\mathrm{mVs}}^{-1}$	627	This work

. Moreover, Figure 14c illustrates the cyclic voltammograms of the ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4/0.5 \mathrm{M}$ at various scan rates, resulting in capacitance values of 627 F/g, 364 F/g, 190 F/g, and 120 F/g at scan rates of 5 mV/s, 10 mV/s, 50 mV/s, and 100 mV/s, respectively. These values are compared in the column bar graphs shown in Figure 14d. This trend suggests that higher scan rates lead to a reduction in the active sites on the inner surface that supports the transitions of the redox reactions. This probable decrease is likely due to the proton diffusion effect occurring within the electrode. The observed decrement in capacitance (C_s) indicates that the surface of the electrode plays a crucial role in fast charging and discharging rates [68]. Additionally, at higher scan rates, the anodic peak shifts toward higher potentials, reflecting the diffusion-controlled capacitive behavior of the ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ electrode.

Figure 14. (a) CV of ${\mathbf{F}\mathbf{e}}_3{\mathbf{O}}_4$ for 0.5 M, 1 M, and 1.5 M precursor concentrations at scan rate of 5 mVs⁻¹. (b) Bar graph of specific capacitance values of each sample for variable molar concentrations. (c) CV of ${\mathbf{F}\mathbf{e}}_3{\mathbf{O}}_4$ for 0.5 M precursor concentration. (d) Bar graph of specific capacitance values of ${\mathbf{F}\mathbf{e}}_3{\mathbf{O}}_4$ for 0.5 M precursor concentration at variable scan rate.

Figure 14. (a) CV of ${\mathbf{F}\mathbf{e}}_3{\mathbf{O}}_4$ for 0.5 M, 1 M, and 1.5 M precursor concentrations at scan rate of 5 mVs⁻¹. (b) Bar graph of specific capacitance values of each sample for variable molar concentrations. (c) CV of ${\mathbf{F}\mathbf{e}}_3{\mathbf{O}}_4$ for 0.5 M precursor concentration. (d) Bar graph of specific capacitance values of ${\mathbf{F}\mathbf{e}}_3{\mathbf{O}}_4$ for 0.5 M precursor concentration at variable scan rate.

2.6.2. Galvanostatic Charge–Discharge Results

The galvanostatic charge–discharge (GCD) curves are an effective way to illustrate the capacitive performance of a capacitor. Figure 15a displays GCD curves obtained from a 0.5 M precursor concentration at different current densities (1, 3, and 5 Ag⁻¹). The curves, which are plotted between a specified voltage range and time, demonstrate a symmetrical and consistent behavior over time. The specific capacitance values measured from the GCD are 602 F/g, 510 F/g, and 440 F/g for current densities of$1 {\mathrm{A}\mathrm{g}}^{-1}, 3 {\mathrm{A}\mathrm{g}}^{-1}, \mathrm{a}\mathrm{n}\mathrm{d} 5 {\mathrm{A}\mathrm{g}}^{-1}$, respectively, as shown in Figure 15b. In GCD curves, the discharge time of ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ at 1 Ag⁻¹ is longer compared to higher current densities, resulting in a greater specific capacitance value. At higher current densities, the availability of inner reactive sites for sustaining redox reactions decreases. The durability of the electrode material is assessed by examining its cyclic stability. During the initial charge or discharge cycle, an initial layer or film forms on the surface of the electrode. As the cycling progresses, the surface of the electrode undergoes restructuring, resulting in the formation of additional active sites. The data from the first cycle reveals important information about the electrode’s specific capacitance, energy storage capabilities, and cyclic stability. Subsequent cycles provide insights into the long-term cyclic behavior of the electrodes. The cyclic stability of the ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ electrodes prepared with a precursor concentration of 0.5 M was evaluated by conducting cyclic voltammetry (CV) for 3000 cycles, as illustrated in Figure 15d. This electrode demonstrates an impressive charge retention of 92% for cyclic voltammetry (CV). This high retention is attributed to the increased number of active sites provided by magnetite nanoparticles, as illustrated in the SEM images. These active sites facilitate rapid redox reactions. The slight decrease in the specific capacitance during cycling may be due to some loss of electrode material [75]. Additionally, the energy density reaches a peak value of $67.725 \mathrm{W}\mathrm{h}/\mathrm{k}\mathrm{g}$, while the maximum power density is recorded at$2227 \mathrm{W}/\mathrm{k}\mathrm{g}$, as shown in Figure 15c.

2.6.3. EIS Results

Electrochemical impedance spectroscopy (EIS) was performed in the frequency range from 10⁵ Hz to 1 Hz to analyze the electrochemical kinetics of the designed electrodes. The Nyquist plots of ${\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4$ prepared at precursor concentrations of 0.5 M, 1 M, and 1.5 M are shown in Figure 16a. The Nyquist plot consists of two distinct regions: a linear portion in the low-frequency region and a semicircular portion in the high-frequency region. The semicircular portion represents the capacitive behavior of the electrode, which is attributed to charge-transfer reactions, while the sloping line corresponds to the Warburg impedance process associated with diffusion-controlled phenomena [76]. The straight line in the EIS graph indicates Warburg resistance, which is related to the diffusion of electrolyte ions within the electrode surface. The semicircular region reflects the charge transfer resistance $\left({\mathrm{R}}_{\mathrm{c}\mathrm{t}}\right)$ during Faradaic reactions, while its intercept with the x-axis represents the solution resistance $\left({\mathrm{R}}_s\right)$, which is a combination of resistances from the electrolyte, electrode, and electrode–electrolyte interface. EIS data fit well with the equivalent circuit shown in the inset of Figure 16a. The calculated ${\mathrm{R}}_{\mathrm{c}\mathrm{t}}$ values for the $\mathrm{F}\mathrm{e}₃\mathrm{O}₄$ prepared at precursor concentrations of 0.5 M, 1 M, and 1.5 M are 4 Ω, 4.5 Ω, and 4.7 Ω, respectively. The analysis of these plots indicates that the increase in ${\mathrm{R}}_{\mathrm{c}\mathrm{t}}$ with higher precursor concentrations corresponds to a larger semicircle. The steepest slope of the vertical portion at the lowest precursor concentration (0.5 M) indicates minimal resistance to charge transfer, which enhances the conductivity of the sample. At the lowest precursor concentration, the smaller nanoparticle size of the electrode minimizes the resistance and maximizes conductivity. This facilitates a higher electron transfer rate at the electrode surface, making the material highly suitable for supercapacitor applications.

Additionally, a graph was plotted between the logarithm of the scan rate (x-axis) and the logarithm of the peak current (y-axis), commonly referred to as the power law, to analyze the material’s behavior. Using this power law, the value of parameter b was calculated, as shown in Figure 16b. When the value exceeds 0.5 and approaches 1, it indicates pseudo-capacitive behavior, which is more suitable for supercapacitors than for batteries. In the present study, the b value was determined to be 0.72, highlighting the remarkable pseudo-capacitive properties of $\mathbf{F}\mathbf{e}₃\mathbf{O}₄$ nanoparticles. Consequently, $\mathbf{F}\mathbf{e}₃\mathbf{O}₄$ prepared at a 0.5 M precursor concentration demonstrates an excellent performance as an electrode material for supercapacitor applications. This superior performance can be attributed to its high surface area, abundant active sites, efficient Faradaic redox reactions, and exceptional charge transfer rate.

3. Materials and Methods

3.1. Experimental Setup

The microplasma setup used in this experiment was developed indigenously and consists of three main components: a power supply, a reaction section, and a gas feed section (as shown in Figure 17). The gas feed section includes a pipeline that connects one end to a gas cylinder and the other end to one of the electrodes in the reaction vessel. This setup allows the gas to be converted into plasma following ignition or discharge. In the reaction section, the reactor and electrode system operate at atmospheric pressure and room temperature under a microplasma jet, which functions as the cathode for this experiment. Meanwhile, the solution in the reaction vessel serves as the anode. During the reaction, the anode is submerged in the solution alongside a carbon strip attached to it to enhance the electrochemical process. This method is also referred to as direct contact liquid microplasma interaction. A direct current (DC) power supply generates plasma discharge within the reaction vessel, creating a closed circuit. To ignite direct current plasma on the surface of a liquid, a stainless steel (SS) capillary tube is used as the cathode. The tube has an inside diameter of 0.4 mm, an outside diameter of 0.6 mm, and a length of 7 cm. It is positioned 4 mm above the surface of the liquid and 2 cm away from the anode within the plasma electrochemical reaction vessel. The anode is a carbon strip, measuring 1 cm in thickness, and is located inside the reaction vessel, which has a diameter of 5 cm and a length of 4 cm. The vessel contains an electrolyte made from a ferrous sulfate solution mixed with 100 mL of distilled water. Ferrous sulfate $(\mathrm{F}\mathrm{e}\mathrm{S}\mathrm{O}₄)$ and distilled water were available in the lab. Nitrogen, which is a non-poisonous gas, was utilized as a plasma source with a constant gas flow rate of 200 sccm. A high-quality DC power supply was also purchased locally with an applied voltage of 15 kV to the capillary tube. A high-voltage probe (Tektronix P6015A, Beaverton, OR, USA) was used to measure the applied voltage between two electrodes. Whereas plasma is kept stable with the help of a ballast resistor (RA = 100 kV). To prepare a molar solution of ferrous sulfate, the appropriate mass in grams was measured using a digital scale. The ferrous sulfate was then dissolved in distilled water with a glass stirrer to ensure that no precipitates settled at the bottom of the reaction vessel.

3.2. Procedure

After dissolution, the sample was placed under a microplasma discharge setup. Various chemical processes began at the plasma–liquid interface zone, which is the most favorable area for synthesizing nanoparticles. The color of the sample solution changed immediately from transparent to a dark yellowish-brown after some time. After 1.5 h of treatment, precipitates formed, which were then subjected to a two-step centrifugation and washing process. An 800 L centrifugal machine (LAB Tech Engineering, Phareska, Thailand) operating at 4000 RPM, 220 V, 50 Hz, and 50 W was utilized for the experiment. The precipitates were initially centrifuged for 10 min. After centrifugation, they were cleaned with distilled water, followed by another centrifugation for 5 min and another washing process. The cleaned precipitates were then separated into a Petri dish and placed in an oven at 200 °C for 30 min to dry. Once dried, they were ground into a very fine powder and stored in sterilized Eppendorf tubes (Hamburg, Germany). The step-by-step process is depicted in Figure 18.

When plasma species, either electrons or positive ions, start their interaction with liquid they turn liquid sample into ionic liquid (IL) sample. Herein, we have used IL as an anode and electrons generated from plasma interacted with liquid surface in different ways depending on their energy. Electrons, after dissolving into liquid, can produce ${\mathrm{H}}_2$, ${\mathrm{H}}_2{\mathrm{O}}_2$, atomic H, OH, and ${\mathrm{H}}^{-}$ by dissociation of water molecules and many other cascade reactions. While synthesizing metal oxide nanostructures iron acts as reducing agent in the precursor (ferrous sulfate). For fabrication of iron oxide nanostructures, overall reactions are presented below [32]:

$${2\mathrm{F}\mathrm{e}\mathrm{S}\mathrm{O}}_4\to {\mathrm{F}\mathrm{e}}_2{\mathrm{O}}_3+{\mathrm{S}\mathrm{O}}_2+{\mathrm{S}\mathrm{O}}_3$$

(25)

$${\mathrm{F}\mathrm{e}\mathrm{S}\mathrm{O}}_4\to {\mathrm{F}\mathrm{e}}^{2+}+{\mathrm{S}\mathrm{O}}_4^{2-}$$

(26)

$${2\mathrm{F}\mathrm{e}}^{2+}+2{\mathrm{H}}_2\mathrm{O}\to {2\mathrm{F}\mathrm{e}}^{3+}+{\mathrm{H}}_2+{\mathrm{O}\mathrm{H}}^{-}$$

(27)

${\mathrm{F}\mathrm{e}}^{2+}$ is further oxidized to ${\mathrm{F}\mathrm{e}}^{3+}$ which combines with water ions ${\mathrm{O}\mathrm{H}}^{-}$ in order to form ferric hydroxide ${\mathrm{F}\mathrm{e}\left(\mathrm{O}\mathrm{H}\right)}_3$. Also, ${\mathrm{F}\mathrm{e}}^{2+}$ combines with water ions ${\mathrm{O}\mathrm{H}}^{-}$ to form ferrous hydroxide ${\mathrm{F}\mathrm{e}\left(\mathrm{O}\mathrm{H}\right)}_2$.

$${\mathrm{F}\mathrm{e}}^{3+}+{3\mathrm{O}\mathrm{H}}^{-}\to {\mathrm{F}\mathrm{e}\left(\mathrm{O}\mathrm{H}\right)}_3$$

(28)

$${\mathrm{F}\mathrm{e}}^{2+}+{2\mathrm{O}\mathrm{H}}^{-}\to {\mathrm{F}\mathrm{e}\left(\mathrm{O}\mathrm{H}\right)}_2$$

(29)

Ferric hydroxide undergoes dehydration process to form FeOOH. Finally, FeOOH and ${\mathrm{F}\mathrm{e}\left(\mathrm{O}\mathrm{H}\right)}_2$ undergo a solid-state reaction to form resulting magnetite nanoparticles.

$${\mathrm{F}\mathrm{e}\left(\mathrm{O}\mathrm{H}\right)}_3\to \mathrm{F}\mathrm{e}\mathrm{O}\mathrm{O}\mathrm{H}+{\mathrm{H}}_2\mathrm{O}$$

(30)

$$2\mathrm{F}\mathrm{e}\mathrm{O}\mathrm{O}\mathrm{H}+{\mathrm{F}\mathrm{e}\left(\mathrm{O}\mathrm{H}\right)}_2\to {\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4+2{\mathrm{H}}_2\mathrm{O}$$

(31)

Whereas, the overall reaction of the synthesis is given below:

$$2{\mathrm{F}\mathrm{e}}^{3+}+{\mathrm{F}\mathrm{e}}^{2+}+{\mathrm{O}\mathrm{H}}^{-}=2{\mathrm{F}\mathrm{e}\left(\mathrm{O}\mathrm{H}\right)}_3+{\mathrm{F}\mathrm{e}\left(\mathrm{O}\mathrm{H}\right)}_2\to {\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4+4{\mathrm{H}}_2\mathrm{O}$$

(32)

3.3. Characterizations of Nanoparticles

X-ray diffractometer (Bruker ${\mathrm{D}}_8$, Billerica, MA, USA) was used along with Cu${\mathrm{k}}_{\mathrm{\alpha }}$ having a wavelength of $(\mathsf{\lambda }$) = ${1.5406\mathrm{A}}^{\circ }$ with 20–80 degrees scanning range with 0.05 step size for studying X-ray diffraction patterns. The morphology of samples was determined by utilizing TESCAN scanning electron microscope (Brno, Czech Republic) model MIRA3 with operating voltage of 15 kV. For this purpose, the synthesized iron oxide nanoparticles were first centrifuged at 10,000 rpm for 15 min in order to obtain pure iron oxide nanoparticles. Before taking SEM images, the centrifuged iron oxide nanoparticles were re-dispersed in deionized water and sonicated at room temperature for 30 min. After this iron oxide solution was dried on a silicon wafer at room temperature. While functional group as well as bond analysis of microplasma-fabricated iron oxide nanoparticles was performed by using FTIR Bruker Tensor II through Fourier-transform infrared spectroscopy technique. A UV-vis spectrum was determined by using JASCO V-750 spectrophotometer (Takyo, Japan). The photocatalytic activity was performed by using SPECORD 210 PLUS- 223F1719C visible light lamp (Analytik Jena AG, Jena, Germany) with 200 W, $\mathsf{\lambda }$ > 420 nm along with methylene blue and methyl orange dye. Electrochemical tests were performed using CHI66OE electrochemical analyzer (Shanghai Chenhua Instruments Co., Ltd., Shanghai, China).

3.4. Photocatalytic Activity

Degradation of methyl orange and methylene blue dye was performed by using iron oxide nanoparticles. Initially, photocatalyst (0.8 g/L) was dissolved in 100 mL of dye solution of 0.03 g/L concentration. Thereafter, the mixture was continuously stirred for 20 min at a temperature of 25 °C in order to achieve adsorption–desorption equilibrium. Finally, the prepared solution (4 mL) was then centrifuged to separate solid catalyst. The remaining clear solution was subjected to UV analysis and then it was irradiated under SPECORD 210 PLUS- 223F1719C visible light lamp with 200 W, $\mathsf{\lambda }$ > 420 nm. The % degradation rate was determined by using Equation (33).

$$\% \mathrm{Degradation} \mathrm{rate}={\displaystyle \frac{{\mathrm{A}}_0-{\mathrm{A}}_{\mathrm{t}}}{{\mathrm{A}}_0}}\times 100$$

(33)

where ${\mathrm{A}}_0$ = initial value of absorption of dye before irradiation and ${\mathrm{A}}_{\mathrm{t}}$ = absorption value after irradiation [77].

$${\mathrm{E}}_{\mathrm{C}\mathrm{B}}=\mathrm{X}-{\mathrm{E}}_{\mathrm{c}}-{\displaystyle \frac{1}{2}}{\mathrm{E}}_{\mathrm{g}}$$

(34)

$${\mathrm{E}}_{\mathrm{V}\mathrm{B}}={\mathrm{E}}_{\mathrm{C}\mathrm{B}}+{\mathrm{E}}_{\mathrm{g}}$$

(35)

where ${\mathrm{E}}_{\mathrm{C}\mathrm{B}}$ and ${\mathrm{E}}_{\mathrm{V}\mathrm{B}}$ are conduction band and valence band edge potentials, respectively. X is Mulliken electronegativity of iron oxide which comes out as 5.76 by using Equation (36). ${\mathrm{E}}_{\mathrm{g}}$ is band gap energy of iron oxide. ${\mathrm{E}}_{\mathrm{c}}$ is free electron energy on hydrogen scale having value of 4.5 eV [55].

$$\mathrm{Mulliken} \mathrm{electronegativity}={{\left(\mathrm{X}\right(\mathrm{A})}^{\mathrm{A}}{\mathrm{X}\left(\mathrm{B}\right)}^{\mathrm{B}}{\mathrm{X}\left(\mathrm{C}\right)}^{\mathrm{C}})}^{{\displaystyle \frac{1}{\mathrm{A}+\mathrm{B}+\mathrm{C}}}}$$

(36)

where $\mathrm{X}\left(\mathrm{A}\right),\mathrm{X}\left(\mathrm{B}\right),\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{X}\left(\mathrm{C}\right)$ are electronegativities of individual elements present in molecule, and A, B, and C are number of atoms present in that molecule.

3.5. Electrochemical Study

In order to investigate electrochemical performance as well as charge storage performance of iron oxide nanoparticles, cyclic voltammetry at variable scan rates within potential window of −0.5 V to 0.5 V was performed. A glassy carbon electrode was used as working electrode, and silver as well as platinum wire were used as reference and counter electrodes. Furthermore, for galvanostatic charge–discharge measurements, 3 M solution of KOH was used. Firstly, the 3 M KOH solution was prepared in a glass beaker by measuring grams of KOH with digital scale and dissolving them in distilled water. Afterwards, by taking this solution in individual beakers iron oxide nanoparticles synthesized at variable molar concentrations (0.5 M, 1 M, and 1.5 M) were mixed in KOH solution. All solutions containing iron oxide and KOH were sonicated and then placed under a CV apparatus. CV analysis was then performed for samples at fixed as well as variable scan rates.

Value of specific capacitance was calculated from Equation (37) [78]

$${\mathrm{C}}_{\mathrm{p}}={\displaystyle \frac{\mathrm{A}}{2\mathrm{m}\mathrm{k}({\mathrm{V}}_2-{\mathrm{V}}_1)}}$$

(37)

Moreover, specific capacitance can also be calculated from charge–discharge curves of electrode [64]

$${\mathrm{C}}_{\mathrm{p}}={\displaystyle \frac{\mathrm{I}\mathrm{x}\mathrm{t}}{\mathrm{m}∆\mathrm{V}}}$$

(38)

where I = charge–discharge value of current calculated in amperes, $∆\mathrm{V}$ = potential range calculated in volts, m = active mass of sample, and t = discharging time in seconds. Specific capacitance is calculated in $\mathrm{F}/\mathrm{g}$. The energy density which is calculated in $\mathrm{W}\mathrm{h}/\mathrm{k}\mathrm{g}$ can be obtained from the following calculation [79].

$$\mathrm{E}={\displaystyle \frac{1}{2}}{{\mathrm{C}}_{\mathrm{p}}∆\mathrm{V}}^2$$

(39)

And power density can be calculated from [79]:

$$\mathrm{P}={\displaystyle \frac{\mathrm{E}}{∆\mathrm{t}}}$$

(40)

where $∆\mathrm{t}$ is discharging time. Power density is calculated in$\mathrm{W}/\mathrm{k}\mathrm{g}$. Efficient Faradaic redox reactions, and exceptional charge transfer rate.

4. Conclusions

This study successfully demonstrated the fabrication of magnetite nanoparticles using an eco-friendly and cost-effective atmospheric pressure microplasma treatment. The concentration of the precursor significantly influenced the properties of the nanoparticles. At a concentration of 0.5 M, smaller-sized iron oxide nanoparticles (ranging from 9 to 40 nm) with a well-defined morphology were achieved, along with improved crystallinity and controlled growth. The enhanced photocatalytic performance resulted in degradation rates of 97.5% for MB and 96.8% for MO. For supercapacitor applications, a high specific capacitance of 627 F/g and an excellent cyclic stability (92% retention after 3000 cycles) were obtained. The lowest charge transfer resistance $\left({\mathrm{R}}_{\mathrm{c}\mathrm{t}}\right)$ was also achieved, indicating a large electroactive area for the 0.5 M sample. These results highlight the potential of microplasma-synthesized iron oxide nanoparticles for advanced environmental and energy storage applications, offering tunable properties for specific needs.