Alaa Mohmmad Soubh ¹*; Jasem Yousef ²; Lorin Ahmad ³; Aziz Al-Hazourri ²

1, Department of Food Technology, Faculty of Technical Engineering, Tartous University, Tartous, Syria

2, Department of Renewable energies, Faculty of Technical Engineering, Tartous University, Tartous, Syria

3, General Commission for Scientific Agriculture Research (GCSAR), Tartus, Syria

 

Manuscript link
http://dx.doi.org/10.30493/DAS.2026.012201

Abstract

In this study, steel industry waste nanocomposites (SICNPs) were used as an activator of hydrogen peroxide (H₂O₂). The SICNPs/H₂O₂ system’s performance was tested for removing Methylene blue (MB) from aqueous solution, primarily through oxidation by hydroxyl radicals (•OH). X-ray diffraction (XRD) patterns of SICNPs showed the presence of associated peaks α-Fe–O at different 2θ angles. SEM image revealed that the SICNPs form aggregated clusters of nanoparticles, indicative of successful size reduction through ball milling and suggesting a high specific surface area conducive to catalytic activity. The effect of pH: (3-9); H₂O₂ dose: (5-45 mmol L^-1); SICNPs dose: (0.5-3 g L^-1) on removal efficiency of MB was studied. Response Surface Methodology (RSM) with a Box-Behnken Design (BBD) was utilized to identify the optimal conditions to achieve the maximum value of the desired response (MB removal efficiency). It was observed that the acidic medium was more conducive to the catalysis process and therefore to the removal process. Based on the RSM model, the relationship between the MB removal and the operating parameters was obtained in polynomial equations with a high degree of compatibility between true values and the predictive values. Further research is needed to fully explore the potential of SICNPs for methylene blue (MB) removal under real-world conditions through pilot-scale studies using actual contaminated wastewater matrices.

Keywords: Methylene blue, Steel industry waste, Nanocomposites, Water, Response surface

Introduction

Dyes are vital components in various industries, and the wastewater generated from these industries is often laden with excess dyes. The release of excess dyes negatively affects the environment and pose serious threats to ecosystems [1]. Methylene blue (MB, C₁₆H₁₈N₃ClS) is one of the most commonly used dyes in many industries[2]. MB polluted water causes unfavorable changes to the aquatic environment as it causes adverse consequences to living organisms when touched or ingested. Methylene blue (MB) is a cationic, primary thiazine dye featuring an aromatic ring system [3], with a molecular weight of 319.85 g mol^-1 and a maximum absorption wavelength (λmax) of 663 nm. These properties render it highly soluble and stable in water under normal conditions. Therefore, the removal or degradation of MB has attracted the attention of researchers and stakeholders.

Various methods have been used for the removal of methylene blue from aqueous solution, including physical and biological treatment, as well as coagulation and agglomeration, separation membranes, absorption, and chemical oxidation [1]. Hydrogen peroxide (H₂O₂) is one of the most widely used oxidants in the remediation and purification of environmental pollutants. The presence of H₂O₂ with ferrous ions (Fe²⁺) contributes to the generation of hydroxyl radicals (^•OH), which is a highly reactive species with exceptional oxidizing capacity. The Fenton process is an advanced oxidation technique widely employed for the degradation of a broad spectrum of organic contaminants in aqueous systems [4]. In recent years, nanomaterials have gained increasing attention as catalysts or enhancers in Fenton-based systems, owing to their high surface area, tunable reactivity, and unique physicochemical properties [5][6].

The steel production process is accompanied by the generation of large amounts of waste. Recycling and reusing steel industry waste (SIW) remain challenging; however, its high iron oxide content makes it a promising material for catalytic activation processes, particularly in processes requiring iron-mediated activation, such as heterogeneous Fenton or Fenton-like reactions [7-10]. In this context, steel industry waste (SIW) has been explored as a soil amendment and plant supplement, leveraging its iron content to support iron fertilization and promote plant growth [11]. Fly ash, bottom ash, and converter slag have also been used to remove pollutants from aquatic environments [12].

Response surface methodology (RSM) is a statistical and mathematical approach widely used to optimize complex processes across various industrial sectors by identifying the optimal combination of multiple input variables to achieve a desired response. RSM has been applied in dye degradation studies to systematically optimize reaction conditions and improve process efficiency [13]. For instance, response surface methodology was used to improve the degradation of basic red 46 dye in a hybrid electrocoagulation and ozonation system [14]. RSM was also applied to improve the biological removal of methylene blue dye from industrial wastewater using sustainable walnut shell biomass as a biosorbent [15].

Building on the aforementioned considerations, this study employs ball-milling to synthesize nanocomposites from steel industry waste (SIW-derived nanocomposites, SICNPs) for catalytic activation of H₂O₂, targeting the removal of methylene blue (MB) from aqueous solutions. Response surface methodology (RSM) was subsequently applied to systematically optimize the methylene blue removal process.

Materials and Methods

Materials

Na₂S₂O₈ (SPI; 99%), NaOH (≥ 97.0 %), HCl (37%), H₂SO₄ (98%), H₂O₂ (30%), and MB (C₁₆H₁₈N₃ClS) were purchased from trusted companies.

Preparation of steel industry waste nanocomposites (SICNPs)

The steel industry waste (by-product) was passed through a 60-mesh sieve (<250 µm), then, a ball mill was used to finely grind the steel industry waste nanocomposites (SICNPs).

SICNPs characteristics and analytical methods

Scanning electron microscope (SEM) and energy dispersive X-ray spectroscopy (EDS) analysis were used to investigate the surface morphology (VEGA3 – TESCAN-Libusina trida, Czech). X-ray diffraction (XRD) patterns of samples were recorded on an X-ray diffractometer (X’Pert PRO MPD, PANalytical Company) equipped with a Cu Kα radiation source (40 kV, 40 mA), over a 2θ angular range of 5° to 80°. A Metrohm 691 pH meter (±0.05 pH unit uncertainty) was used to measure the pH of solutions.

Methylene blue concentration was determined spectrophotometrically by measuring absorbance at its characteristic maximum wavelength of 664 nm. First, various concentrations of MB dye were prepared. Then, their absorption was measured at a maximum wavelength of 664 nm using spectrophotometer. The calibration curve was established by plotting absorbance values against the corresponding methylene blue concentrations. Finally, the dye concentration in the samples was calculated by substituting their absorbance values into the equation of the calibration curve [2].

Oxidation experiments

Batch experiments were conducted using 40 mL of methylene blue (MB) solution (10 mg L⁻¹, unless otherwise specified) in a 100 mL glass beaker. A predetermined amount of SICNPs and H₂O₂ was added, and the mixture was stirred magnetically at 800 rpm to ensure homogeneous dispersion. After 60 minutes of reaction, a sample was withdrawn, centrifuged (or filtered, if applicable), and analyzed to determine the residual MB concentration.

The effect of the following factors pH: (3, 6, and 9); H₂O₂ dose: (5, 25, and 45 mmol L^-1); SICNPs dose: (0.5, 1.75, and 3 g L^-1) on MB removal efficiency was studied at room temperature. The removal efficiency of MB was estimated as follows:

Removal efficiency (%) = [(C_i-C_f )/C_i]×100

Where C_i and C_f indicate the initial and final MB concentration, respectively.

Response Surface Methodology (RSM) employing a Box–Behnken Design (BBD), a rotatable, second-order experimental design, was used to model and optimize the process. BBD facilitates efficient estimation of quadratic effects with fewer experimental runs compared to full factorial designs, making it well-suited for systems involving multiple interacting variables:

Where R is the predicted response for removal efficiency, β₀ is the intercept parameter, β_i, β_ii, and β_ij are parameters for linear, quadratic, and interaction factor effects, x_i and x_j are independent variables and ε is the error.

The analysis of the variance (ANOVA) was used to determine the homogeneity between the model and the empirical results. The F-test was applied to verify whether the model could predict a significant variation in the experimental data. The probability (p-value) was used to estimate whether F is large enough to indicate statistical significance [16]. The model was developed using the software Design Expert v.10.0.3. The levels of the selected variables (low, center, and high) are denoted as -1, 0, and 1, respectively (Table 1).

A total of 17 experimental runs were conducted according to the Box–Behnken Design (BBD), including five replicates at the central point to estimate experimental error and assess model reproducibility. The resulting empirical data, obtained under the selected BBD conditions, were used for subsequent statistical analysis (Table 2).

Results and Discussion

SICNPs characterization

The XRD pattern of the SICNPs (Fig. 1 A) reveals characteristic diffraction peaks of α-Fe–O, observed at 2θ values of approximately 18.18°, 24.04°, 30.18°, 35.51°, 43.13°, 49.51°, 53.60°, 54.16°, 57.14°, 62.71°, and 74.23°. These results confirm the crystalline nature and predominant phase composition of the synthesized nanocomposites [17].

SEM image (Fig. 1 B) shows that SICNPs forms clusters of nanoscale particles, indicating efficient particle size reduction through ball milling. As a consequence, this method can provide a high surface area for the activation, promoting effecient advanced oxidation processes (AOPs) for breaking down organic pollutants in water, wastewater, and soil remediation. In fact, such iron-rich nanomaterials often facilitate iron’s redox cycle between Fe(II) and Fe(III) states on the nano-surface, leading to efficient degradation with lower costs and environmental impact. Importantly, their iron-oxide composition (e.g., magnetite or hematite) not only ensures catalytic activity but also enables magnetic separation and recovery, enhancing process sustainability and cost-effectiveness [18].

The EDS analysis of SICNPs (Fig. 1 C), shows that iron constituted the largest proportion of the elemental analysis (70.32%). This high iron content supports the SICNPs suitability for catalytic applications, particularly in iron-driven processes such as Fenton and Fenton-like reactions. These morphological and structural observations highlight SICNPs potentials in environmental remediation, catalysis, and resource recovery strategies.

Response surface model

Analysis of variance was utilized to estimate the validation of models and significance of variables (Table 3). The model exhibited a high F-value of 65.64, indicating its overall significance. Furthermore, the linear terms (A, B, C), interaction terms (AB, AC, BC), and quadratic terms (A², B², C²) were all statistically significant (p<0.05) [19]. Moreover, The Pred R² of 0.9717 is in reasonable agreement with the Adj R² of 0.9959; indicating good model consistency and predictive capability. Additionally, an Adequate Precision ratio of 52.261 suggests a strong signal-to-noise ratio, confirming that the model is robust enough to effectively navigate the design space.

Based on the RSM model, the relationship between the MB removal and the operating parameters (pH, H₂O₂ dose, and SICNPs dose) was expressed through a second-order polynomial equation:

MB removal (%) = 94.2 – 25.13A + 21.13B + 14.5C – 10.75AB – 13.5AC + 15.5BC – 24.23A² -30.73B² – 23.47C²

Where A is pH, B is H₂O₂ dose, and C is SICNPs dose.

The 3D surface plots of MB removal efficiency at center points showed thatthe removal efficiency was better in an acidic medium (Fig. 2 A). This observation is attributed to the fact that iron oxides decompose to give iron ions [20-27]:

Fe₃O₄ + 8H⁺ → 2Fe³⁺ +Fe²⁺ + 4H₂O

Fe₂O₃ + 6H⁺ → 2Fe³⁺ + 3H₂O

Upon addition of H₂O₂, ferrous ions (Fe²⁺) catalyze its decomposition to generate highly reactive hydroxyl radicals (•OH):

Fe²⁺ + H₂O₂ → Fe³⁺ + HO^• + OH^–

These •OH radicals subsequently attack the methylene blue (MB) molecules, leading to their oxidative degradation and eventual mineralization, as outlined in the proposed reaction pathway [4]:

HO^• + MB → CO₂ + H₂O

Furthermore, ferric ions (Fe³⁺) react with H₂O₂ and hydroperoxyl radicals (HO₂•) to regenerate ferrous ions (Fe²⁺), thereby sustaining the catalytic cycle and maintaining active activation capacity [28]:

Fe³⁺ + H₂O₂ → Fe²⁺ + HO₂• + H⁺

Fe³⁺ + HO₂• → Fe²⁺ + O₂ + H⁺

Overall, optimal methylene blue (MB) removal efficiency (98%) was achieved under a pH level of 3, H₂O₂ concentration of 25 mmol L^-1, and a SICNPs dosage of 3 g L^-1. The concentration of the H₂O₂ dose is of great importance to the effectiveness of MB removal (Fig. 2 A and C). At low H₂O₂ dose, the rate of ^•OH radicals formation decreases. Increasing the H₂O₂ dose enhanced the removal efficiency up to an optimal point, attributable to the higher concentration of hydroxyl radicals (•OH) generated. However, an excessive H₂O₂ dose can scavenge •OH radicals or compete with the target pollutant for reactive species, potentially diminishing removal efficiency [29]. The generation of ferrous ions increases by increasing SICNPs dose (Fig. 2 B and C). However, an excessive SICNPs dose causes consumption of active ^•OH radicals, leading to decreased removal efficiency of MB [30].

The findings presented here demonstrate that iron-rich nanocomposites derived from steel industry waste (SICNPs) possess strong structural, compositional, and catalytic attributes for efficient methylene blue degradation via Fenton-like advanced oxidation processes. The crystalline α-Fe–O phase, high iron content, and nanoscale clustered morphology collectively enable effective •OH radical generation under optimized acidic conditions. Response surface modelling confirmed the critical interplay of pH, H₂O₂ dose, and SICNPs dosage in governing removal efficiency. While the model exhibits excellent predictive accuracy and the material shows promise for sustainable water treatment, certain limitations should be tackled in future research. The current investigation focused solely on MB as a model pollutant under controlled laboratory conditions. However, competing ions and the various natural organic matter that are usually found in wastewater may alter performance. Moreover, long-term stability, potential iron leaching, and ecotoxicity of residual nanoparticles require further assessment. The elemental composition of SICNPs, including potential heavy and toxic contaminants such as lead, should be thoroughly evaluated to identify optimal industrial sources suitable for water treatment applications. Consequently, future work should expand testing to diverse organic contaminants and evaluate SICNPs in continuous-flow or pilot-scale systems. Furthermore, exploring surface modifications might be necessary in order to enhance activity in near-neutral pH environments, thereby broadening applicability to real-world remediation scenarios while advancing circular economy goals in industrial waste reuse.

Conclusion

The XRD and EDS analyses of showed SICNPs is rich in iron compounds, this indicates its potential use in fields that require elemental iron, particularly in environmental remediation (e.g., catalytic degradation of pollutants) and agricultural uses (e.g., as a micronutrient source or soil amendment). The results showed the positive effect of SICNPs as H₂O₂ activator, which supports its potential use as a catalyst with other oxidizing agents such as persulfates. An excessive amount of both H₂O₂ and SICNPs reduced the removal efficiency due to radical scavenging effects. Future work should include pilot-scale testing, broader contaminant trials, and surface modifications for near-neutral pH applications.

Conflict of interest statement
The authors declared no conflict of interest.
Funding statement
The authors declared that no funding was received in relation to this manuscript.
Data availability statement
The authors declared that all related data are included in the article.

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Cite this article:

Soubh AM, Yousef J, Ahmad L, Al-Hazourri A. Steel industry waste nanocomposites for catalytic degradation of methylene blue: Optimization and efficiency. DYSONA-Applied Science. 2026;7(2):200–8. doi: 10.30493/das.2026.012201