Novel phase-controlled Al_2.66O_3.99/iron oxide nanocomposites for rapid Cu(II) removal from acidic water

Abstract

In this study, alumina/iron oxide nanocomposites are synthesized via a simple, cost-effective co-precipitation method and investigated for the removal of copper ions from acidic aqueous media. Five samples with varying iron oxide phase contents are prepared by varying the precursor molar ratios. Structural analysis confirms the coexistence of Al₂.₆₆O₃.₉₉ and iron oxide phases, while Rietveld refinement reveals that the iron oxide phase fraction increases from 0.2% to 67.2% across the synthesized samples. The nanocomposites are characterized in detail using a range of analytical methods, including structural, morphological, optical, magnetic, and elemental analyses. Magnetic measurements indicate soft-magnetic behavior, with an enhanced response observed in the sample with the highest iron oxide content. The experimental results demonstrate rapid uptake of Cu(II), with significant removal occurring within the first few minutes and removal efficiencies exceeding 90% after 90 min for most samples. Nonlinear kinetic analysis indicates that the pseudo-second-order model provides a good description of Cu(II) adsorption behavior, while complementary characterization elucidates the underlying adsorption interactions. Owing to their tunable phase composition and effective Cu(II) adsorption performance, these alumina/iron oxide nanocomposites show potential for wastewater treatment in mildly acidic aqueous media.

Clinical trial number: Not Applicable.

Introduction

There is currently considerable interest in the properties and behavior of nanoscale semiconductors, as reducing their physical dimensions can lead to significant changes in structural characteristics and the emergence of unusual and functional properties. As particle size decreases, the surface-to-volume ratio increases substantially, resulting in enhanced chemical and biological reactivity. Furthermore, at the nanoscale, material behavior gradually deviates from classical laws, while quantum effects begin to play a crucial role in determining their physical properties. These unique characteristics have attracted widespread attention to nanomaterials across various fields, including sensors, photocatalysts, energy storage systems, and, in particular, the removal of environmental pollutants. In pollutant removal processes, contaminants present in water can be adsorbed onto the pores and active sites of nanoparticles, and owing to the high specific surface area of nanoparticles, nanocomposites, and quantum dots, the pollutant removal efficiency can be significantly enhanced [1].

In recent years, the removal of metallic pollutants from aqueous environments has become a major environmental challenge because potentially toxic metals, unlike many organic contaminants, are non-biodegradable and exhibit a strong tendency toward bioaccumulation in living organisms. Various potentially toxic metal ions, including copper, nickel, lead, mercury, chromium, zinc, and cadmium, are widely detected in industrial wastewater streams [2]. These elements can disrupt protein function, genetic processes, and redox balance in biological systems, thereby causing severe adverse effects on human health and other living organisms [3, 4]. Among these metals, copper ions (Cu²⁺) are of particular concern due to their widespread presence in municipal and industrial effluents [5]. Although copper is an essential trace element for the human body, concentrations exceeding the permissible limit (greater than 2 mg/L) may lead to severe health complications such as headaches, neurological disorders, depression, and learning disabilities [6]. Therefore, the development of efficient approaches for the removal of copper ions from aqueous environments is of significant importance. Among the various water treatment techniques, adsorption has been recognized as one of the most effective and widely employed methods for the removal of potentially toxic metals due to its simplicity, operational flexibility, relatively low cost, and high efficiency [7]. Therefore, metal oxide nanostructures have attracted considerable attention owing to their high specific surface area, suitable porosity, and abundance of surface-active sites [8].

Alumina nanoparticles are considered among the most important metal oxides in this field due to their high chemical and thermal stability, good biocompatibility, large specific surface area, and surface modification capability. These properties have enabled their application in a wide range of areas, including potentially toxic metals removal, selective adsorption of chemical species such as arsenic and fluoride, sulfur removal from gas streams, catalysis, mechanical reinforcement of ionic gels, electronics, metallurgy, and separation and purification processes [9–11]. In addition, the high transparency of alumina layers is one of the key characteristics responsible for their extensive applications in optics [4]. Numerous studies demonstrated that alumina nanoparticles exhibit promising performance in pollutant removal from aqueous environments. For instance, in one study, alumina nanoparticles synthesized via the sol–gel method were utilized for the removal of contaminant ions from water, and the results revealed that controlling the synthesis conditions plays a critical role in determining the structural properties of the nanoparticles and consequently enhancing pollutant removal efficiency [12]. Moreover, in 2024, researchers synthesized alumina nanoparticles with different morphologies and employed them to improve the mechanical strength of ionic gels. The findings indicated that the morphology and structure of the nanoparticles directly influence the final properties of the system [11].

Despite the numerous advantages of alumina nanoparticles, their application in aqueous environments is associated with certain limitations. One of the major challenges is the difficulty of separating and recovering the adsorbent from solution after the treatment process, which can reduce operational efficiency and increase process costs [13]. Furthermore, some nanoadsorbents tend to aggregate in aqueous media, leading to a reduction in active surface area and consequently limiting adsorption performance [14]. Therefore, the development of nanocomposites that simultaneously possess high specific surface area, facile recoverability, and good stability in aqueous environments is of considerable importance. In this context, the utilization of magnetic nanoparticles, particularly iron oxide nanoparticles, has attracted significant attention. Iron oxide nanoparticles have found extensive applications in water treatment and pollutant removal owing to their favorable magnetic properties, low toxicity, excellent biocompatibility, chemical stability, and rapid separation capability under an external magnetic field [15, 16]. Furthermore, it has been reported that combining iron oxide nanoparticles with porous nanostructures can increase the number of active adsorption sites, improve mass transfer, and induce synergistic effects during pollutant removal processes [17].

The co-precipitation method is considered one of the most common and effective techniques for synthesizing alumina nanoparticles and other oxide nanostructures. The major advantages of this method include procedural simplicity, rapid synthesis, effective control over particle size and morphology, uniform particle size distribution, and low cost [18]. In 2017, γ-alumina nanoparticles were synthesized via the co-precipitation method, while cobalt nanoparticles were prepared through direct reduction. Subsequently, a cobalt/polyvinyl alcohol/γ-alumina nanocomposite was fabricated and utilized for catalytic applications [19]. These investigations indicate that combining alumina nanoparticles with other nanomaterials can significantly improve their functional properties and enhance their application efficiency. Therefore, some studies have focused on the synthesis of alumina- and iron oxide-based nanocomposites. For example, an iron–aluminum nanocomposite was synthesized through a green synthesis route using clove extract and evaluated as a fluoride adsorbent [20]. The results demonstrated that the synthesized nanocomposite exhibited promising pollutant removal performance from water, and its adsorption behavior followed the pseudo-second-order kinetic model [20].

In this study, we report the rational design and facile synthesis of a novel Al₂.₆₆O₃.₉₉/iron oxide magnetic nanocomposite, specifically engineered to overcome the inherent challenges of potentially toxic metals remediation in acidic aqueous environments. While conventional alumina-based adsorbents often suffer from limited recoverability and reduced stability, our approach synergistically integrates the high surface reactivity of the Al-oxide (Al₂.₆₆O₃.₉₉) phase with the robust magnetic functionality of iron oxide. A systematic investigation is conducted to evaluate the influence of precursor molarity on the phase evolution and physicochemical properties of the resulting nanostructures. Beyond achieving efficient magnetic separation, the synthesized nanocomposites demonstrate an exceptional adsorption capacity for Cu(II) ions within remarkably short contact times, even under mildly acidic conditions-a critical bottleneck in contemporary wastewater treatment. To provide a deep understanding of the structure-performance correlation, a comprehensive suite of characterizations is employed, including XRD, FESEM/EDX mapping, BET/BJH, DRS/Tauc, TGA/DTA, and VSM. Furthermore, the underlying adsorption mechanism is rigorously evaluated through post-adsorption FTIR and FESEM/EDX analyses. This work not only introduces a high-performance adsorbent but also provides new insights into the synergistic mechanisms governing metal ion sequestration in complex, low-pH matrices.

Material and method

Aluminum nitrate nonahydrate (Al(NO₃)₃.9H₂O, 95%), ammonium bicarbonate (NH₄HCO₃, 98%), iron (III) nitrate nonahydrate (Fe(NO₃)₃.9H₂O), CuSO₄.5H₂O, and NaOH were purchased from Merck company and used as received, without any further purification. Alumina/iron oxide nanocomposites were synthesized in a single step via a co‑precipitation route. As follows:

Following previous reports indicating the high efficacy of alumina nanoparticles synthesized via co-precipitation at a 0.1 M precursor concentration for Cu(II) removal [21], the total molarity of the metal precursors in this study was maintained constant at 0.1 M. Specifically, the combined concentration of aluminum nitrate nonahydrate and iron (III) nitrate nonahydrate was fixed at this threshold to investigate the influence of varying iron content on the composite’s properties. To achieve this, the molarity of the iron precursor was systematically varied from 0.003 to 0.05 M (0.003, 0.005, 0.007, 0.01, and 0.05 M), while the aluminum precursor concentration was adjusted accordingly to ensure a constant total molarity. In a typical procedure, 60 mL of the aluminum nitrate solution (varying concentration) and 60 mL of the ammonium bicarbonate (0.1 M) were added dropwise to a beaker containing the iron nitrate solution at the specified concentration. This controlled stoichiometric approach was adopted to isolate the effect of the Al/Fe molar ratio on the resulting nanostructure and its subsequent adsorption performance.

In this step, the pH was adjusted to 8 by the gradual addition of a 2 M NaOH solution, and the final solution was stirred for 1 h. The suspension was then placed in an oven at 70 °C for 3 h to form a precipitate. Subsequently, the obtained precipitate was washed 3 times with DI water and 2 times with ethanol by centrifugation to remove residual ionic and organic impurities. The final precipitate was then placed in a 10-hour drying process at 70 °C in an oven, and after drying, it was calcined at 550 for 2 h in air. The resulting powders were labeled C3, C5, C7, C10, and C50 based on the concentrations of iron nitrate nonahydrate (×10^− 3) used in the precursor solution. The final powders were then subjected to various characterization techniques to elucidate their structural, optical, and other relevant physical properties.

Results and discussion

In this section, the influence of the molar concentration of iron oxide and alumina precursors on the physicochemical properties of alumina/iron oxide nanocomposites synthesized via the co-precipitation method is systematically investigated. Specific emphasis is placed on the structural and optical evolution of the resulting materials. Following a comprehensive characterization to elucidate the fundamental physical properties of the synthesized nanocomposites, their performance as adsorbents for the removal of copper ions (Cu²⁺) from aqueous solutions is evaluated. Furthermore, the adsorption kinetics are analyzed to understand the underlying mechanisms, and the post-adsorption characteristics of the nanoparticles are examined to assess the structural stability and interaction during the decontamination process. The corresponding results are presented and discussed in detail in the following subsections.

Thermal analysis

The thermal behavior of the synthesized samples was first investigated by DTA/TGA analysis in order to determine an appropriate calcination temperature for subsequent experiments. The results are shown in Fig. 1. The obtained DTA curves reveal that the thermal evolution of the samples strongly depended on the iron‑to‑aluminum precursor’s concentration ratios. For sample C3, a single thermal peak is observed around 500 °C. With increasing iron precursor concentration, additional thermal events appear at lower temperatures and gradually shift toward the range of 320–350 °C. This shift toward lower temperatures can be attributed to the enhanced nucleation rate, accelerated decomposition of precursor species, and facilitated formation of oxide phases induced by the higher iron content. Nevertheless, despite the appearance of low-temperature thermal events, a weak thermal peak near 500 °C is still observed in most samples, indicating the continuation of structural transformations such as completion of crystallization, removal of residual organic species, and final phase stabilization.

Fig. 1.

TGA/DTA profiles of Al_2.66O_3.99/iron oxide nanocomposites synthesized by co‑precipitation at different iron‑to‑aluminum precursor ratios

Therefore, a calcination temperature of 550 °C was adopted for all specimens to ensure complete thermal decomposition and the evolution of stable crystalline phases. This temperature was maintained constant across the entire series to isolate the influence of the iron precursor concentration and eliminate calcination temperature as a confounding experimental variable. Such a systematic approach facilitates a more rigorous and reliable comparison of the structural, morphological, and functional attributes of the synthesized nanocomposites.

Structural analysis

The crystalline nature of the synthesized samples was examined by X‑ray diffraction (XRD), and the diffraction patterns were analyzed using X’Pert software. Comparison of the obtained diffraction patterns with reference data indicates the presence of two crystalline components: Al_2.66O_3.99 and a cubic spinel iron oxide phase. The reflection peaks shown in Fig. 2 closely match the standard JCPDS reference cards No. 96-101-0462 and 01-075-0449, corresponding to γ-Al_2.66O_3.99 and Fe₃O₄, respectively. The iron-containing reflections are consistent with a cubic spinel iron-oxide phase. However, because cubic Fe₃O₄ and γ-Fe₂O₃ possess highly similar crystal structures and XRD patterns, the XRD results alone cannot unambiguously distinguish between these two phases. Accordingly, the iron-containing phase is not assigned specifically to either Fe₃O₄ or γ-Fe₂O₃ based solely on the XRD results and is therefore referred to as iron oxide throughout the manuscript. In addition, the diffraction peaks in the XRD patterns are significantly broad with low intensity, which is consistent with previous studies [22], indicating very small crystallite sizes and a relatively low degree of crystallinity in the products.

Fig. 2.

The XRD patterns of Al_2.66O_3.99/iron oxide nanocomposites synthesized by co‑precipitation at different iron‑to‑aluminum precursor ratios

A systematic shift in peak positions is also observed as a function of precursor molar ratio; the possible causes of these shifts are discussed below. The observed peaks arise from diffraction by specific sets of atomic planes. The positions and intensities of these reflections depend on lattice parameters, atomic arrangement, and sample composition [23]. A shift in diffraction peak positions generally signifies a change in the interplanar spacing of the crystal lattice. Such changes may be induced by several factors, including variations in composition or elemental content. For example, in alloys or solid solutions, changes in the constituent ratios can cause lattice expansion or contraction, leading to systematic shifts in peak positions in the XRD pattern [24]. Temperature can also play a role: thermal expansion or phase transitions modify atomic spacings and crystal symmetry, thereby affecting both peak positions and intensities [25]. External or internal stresses may deform or distort the lattice, altering the positions and shapes of the diffraction peaks. Therefore, for the present alumina/iron oxide synthesized samples prepared with different molar percentages of the iron precursor relative to alumina, the observed peak shifts are reasonable and expected. The peak displacements evident in Fig. 2 can be attributed to structural variations arising from the differing precursor ratios used during synthesis.

To quantify the structural parameters, the XRD patterns of the samples were refined using MAUD software without prior smoothing or filtering, and the results are summarized in Table 1. The refined results show an increasing fraction of the cubic spinel iron-oxide phase as the iron-to-aluminum precursor ratio increases, demonstrating that the phase composition can be systematically controlled by varying this ratio.

Table 1.

Phase percentages and space groups of Al_2.66O_3.99/iron oxide nanocomposites calculated using MAUD software for both constituent

Sample ID	Alumina phase (%)	Iron oxide phase (%)	Space group/ cell parameter (alumina phase, cubic)	Space group/ cell parameter (iron oxide phase, cubic)
C3	99.8	0.2	F m -3 m/a = 4.16	F d -3 m:1/a = 8.36
C5	98.31	1.69	F m -3 m/a = 3.88	F d -3 m:1/a = 8.32
C7	97.34	2.66	F m -3 m/a = 3.95	F d -3 m:1/a = 8.32
C10	96.5	3.5	F m -3 m/a = 3.97	F d -3 m:1/a = 8.32
C50	32.8	67.2	F m -3 m/a = 4	F d -3 m:1/a = 8.32

Morphology (FESEM)

The FESEM images of the Al_2.66O_3.99/iron oxide nanocomposite, synthesized with varying molar ratios of iron oxide precursor to alumina precursor, are depicted in Fig. 3. The results demonstrate that the morphological evolution is highly sensitive to the Fe(NO₃)₃ to Al(NO₃)₃ molar ratio, with distinct structural transformations observed across different concentrations. While previous studies indicate that pure alumina and iron oxide nanoparticles typically exhibit spherical morphologies when synthesized individually via co-precipitation [21, 26], the present Al_2.66O_3.99/iron oxide nanocomposites show significant alterations in both shape and size. This divergence suggests a synergistic interaction between the two oxide phases during the simultaneous nucleation and growth processes.

Fig. 3.

The FESEM images of Al_2.66O_3.99/iron oxide nanocomposites synthesized by co‑precipitation at different iron‑to‑aluminum precursor concentration ratios, including (a) C3, b C5, c C7, d C10, and (e) C50

The particle size distribution for each molar ratio was statistically analyzed using the methodology described in [27]. The resulting histograms, along with their corresponding log-normal distribution fits, are provided as insets in Fig. 3. Quantitative analysis reveals that the average diameters for samples C5 and C50 are 15.27 nm and 11.6 nm, respectively. For sample C7, the specific morphological features precluded a reliable calculation of these parameters. Nevertheless, based on both qualitative FESEM observations and quantitative size metrics, the synthesized products are definitively classified as nanocomposites. Additionally, the average thickness for samples C3 and C10 was measured at 22.57 nm and 18.83 nm, respectively. The calculated standard deviations for samples C3, C5, C10, and C50 are 0.73 nm, 0.01 nm, 1.29 nm, and 0.19 nm, respectively. These analyses were conducted independently for each sample, accounting for their unique morphological characteristics.

A detailed clarification of the FESEM images reveals that the evolution of particle morphology in the alumina/iron oxide nanocomposites is governed by coupled nucleation-growth dynamics and compositional effects causing from the varying precursor ratios. When alumina or iron oxide is synthesized individually, both systems mainly form spherical nanoparticles, indicating isotropic growth under the selected co‑precipitation conditions. This behavior is consistent with previous reports showing that both γ‑Al_2.66O_3.99 and iron oxide tend to form uniformly spherical nanoparticles under controlled precipitation and drying conditions [28, 29]. At low concentrations of the iron precursor (e.g., C3 and C5), some pronounced morphological deviations emerge. This can be attributed to competitive nucleation between aluminum and iron hydroxide species, and their differing hydrolysis constants. Such competition often results in heterogeneous nucleation, modified growth rates, and broader size distributions-effects frequently observed in mixed metal‑oxide nanostructures synthesized via co‑precipitation [12]. The presence of Fe‑rich nuclei likely influences the aggregation pattern of alumina crystallites, producing more irregular particle shapes. At higher Fe precursor ratios (C7 and C10), the morphology becomes increasingly distinct from the spherical reference state. This trend aligns with reports in the literature indicating that Fe₂O₃ domains can act as structural modifiers, altering the aggregation, coalescence, and growth pathways of alumina nanoparticles during composite formation [6]. Additionally, differences in the hydrolysis and condensation kinetics of Fe³⁺ and Al³⁺ ions generate local gradients in pH and supersaturation, which are known to induce anisotropic or clustered growth patterns in mixed oxide nanoparticles [30].

Overall, the FESEM analysis confirms that the morphology of the alumina/iron oxide nanocomposites is not simply the sum of the individual oxides but reflects synergistic interactions during co‑precipitation. These findings underscore the sensitivity of particle morphology to precursor ratios and demonstrate that fine control of the Fe/Al ratio provides an effective approach for tailoring nanoparticle structure for targeted functional applications.

Elemental mapping (MAP) analysis

Table 2 presents the elemental mapping (MAP) images of the alumina/iron oxide nanocomposites synthesized with different molar ratios of iron nitrate nonahydrate to aluminum nitrate nonahydrate precursors. The MAP images distinctly indicate the presence of aluminum (Al), iron (Fe), and oxygen (O) in all nanocomposite samples. Furthermore, the spatial distributions of these elements are uniform throughout the scanned regions, indicating a homogeneous dispersion of iron oxide within the alumina matrix. The absence of elemental dissociation or localized agglomeration suggests that the co‑precipitation method employed in this work is effective in producing well‑mixed alumina/iron oxide nanocomposites.

Table 2.

Elemental MAP images of Al_2.66O_3.99/iron oxide nanocomposites synthesized by co‑precipitation at different iron‑to‑aluminum precursor ratios

Elemental composition (EDX) analysis

The elemental composition of the synthesized nanocomposites was further investigated using energy‑dispersive X‑ray spectroscopy (EDX), and the results are shown in Fig. 4 along with bar charts representing the atomic percentages of the detected elements in each sample. As observed in Fig. 4, the synthesized samples consist exclusively of aluminum, iron, and oxygen, in agreement with the expected composition of alumina/iron oxide nanocomposites. No impurity elements were detected within the sensitivity limits of the EDX technique. These results are fully consistent with the MAP analysis and further confirm the successful formation and compositional purity of the nanocomposites. Also, an increase in the iron percentage is observed with the increment of the precursor ratio, indicating the ability to control the iron content in the nanocomposite by varying the precursor ratio.

Fig. 4.

EDX spectra of Al_2.66O_3.99/iron oxide nanocomposites synthesized by co‑precipitation at different iron‑to‑aluminum precursor ratios: a C3, b C5, c C7, d C10, and (e) C50. The elemental atomic percentages of the samples are also displayed in the inset of the EDX spectra as pie charts

Optical properties (DRS analysis)

Figure 5a shows the diffuse reflectance spectra (DRS) of alumina/iron oxide nanocomposites synthesized by co‑precipitation at different iron‑to‑aluminum precursor ratios. As previously reported, the bare alumina nanoparticles prepared in the absence of the iron precursor exhibit a high reflectance across the visible region, consistent with the characteristic weak absorption of a wide-band-gap semiconductor [21]. However, the results in Fig. 5a demonstrate that the incorporation of the iron precursor into the synthesis process induces a significant transition, and the resulting nanocomposites become nearly opaque in the UV region with substantial absorption extending into the visible range. This enhanced optical response can be attributed to the introduction of iron oxide–related electronic states within the alumina matrix, which facilitate low-energy photon absorption and the corresponding redshift of the absorption edge.

Fig. 5.

a DRS, b Tauc plot and direct band gap energy, and (c) Tauc plot and indirect band gap energy of Al_2.66O_3.99/iron oxide nanocomposites samples synthesized by the co-precipitation method at different iron‑to‑aluminum precursor ratios

The direct and indirect optical band‑gap energies, , can be determined using the Tauc function and the corresponding plots [31], as presented in Fig. 5b and c. The direct and indirect band‑gap energies of Al_2.66O_3.99/iron oxide nanocomposites synthesized with different iron‑to‑aluminum precursor ratios are summarized in Table 3. The results indicate that variations in the iron precursor concentration influence the band‑gap magnitude.

Table 3.

Direct and indirect optical band‑gap energies of the synthesized Al_2.66O_3.99/iron oxide nanocomposites

sample	Direct band gap energy (eV)	Indirect band gap energy (eV)
C3	3.66	2.74
C5	3.43	2.44
C7	3.37	2.51
C10	3.35	2.67
C50	1.86	1.4

It can be observed that variations in the iron precursor concentration perceptibly modulate the band-gap magnitude. The reported direct and indirect band-gap energies for Fe₃O₄ nanoparticles are commonly in the ranges of ~ 1.86–3.66 eV and ~ 1.4–2.74 eV, respectively, while bare alumina nanoparticles exhibit much a significantly wider band gap (~ 5 eV); the integration of Fe₃O₄ into the alumina matrix induces a significant narrowing of the optical band gap [32–34]. This reduction in of Al_2.66O_3.99/iron oxide nanocomposites and the transformation into a semiconductor material is indicative of the successful formation of a composite system where the iron oxide phase effectively introduces intermediate energy levels, thereby shifting the optical activation threshold toward the visible spectrum.

The reduction in the direct and indirect band-gap energies of the alumina/iron oxide nanocomposites can be ascribed to the electronic interaction between the wide-band-gap alumina matrix and the narrow-band-gap magnetite phase.

The presence of mixed-capacity Fe²⁺/Fe³⁺ ions in the magnetite structure, characterized by partially occupied 3d orbitals, generates localized electronic states near the conduction and valence bands of the alumina matrix. These states effectively reduce the transition energy and enhance visible-light absorption, a phenomenon widely documented for transition-metal-doped oxide nanostructures and Fe₃O₄ nanoparticles [35]. Furthermore, heterojunction effects, resulting from the formation of alumina/iron oxide nanocomposites, create localized interface states that facilitate sub-band-gap electronic transitions and improve optical absorption in the visible region. Such interfacial electronic reconstruction is a common phenomenon in mixed metal-oxide nanocomposites composed of narrow- and wide-band-gap oxides [36]. Additionally, oxygen vacancies, as one of the structural defects resulting from the co-precipitation process and also the calcination process, are another important factor for electronic excitation. The presence of oxygen vacancies in iron oxide-containing systems has been shown to influence their electronic and optical properties by modifying the local electronic density of states [37]. Therefore, the synergy between Fe-induced mid-gap states, interfacial reconstruction, and defect-related energy levels explains the pronounced reduction in both direct and indirect band-gap energies.

Porosimetry studies and pore‑type analysis (BET and BJH)

The specific surface area of the Al_2.66O_3.99/iron oxide nanocomposites is a key parameter governing their adsorption performance. In addition, characterization of the pore size and pore structure provides valuable insight into the textural properties of these materials. Therefore, nitrogen adsorption–desorption analyses (BET and BJH) were carried out for all synthesized nanocomposites by the co-precipitation method at different iron‑to‑aluminum precursor ratios, and the resulting isotherms are shown in Fig. 6a. Comparison of the obtained hysteresis loops with the IUPAC classification reveals that all of Al_2.66O_3.99/iron oxide nanocomposites exhibit H3‑type hysteresis loops, which are generally associated with slit‑shaped pores originating from aggregated plate‑like particles. Furthermore, the overall shapes of the adsorption–desorption curves indicate type IV/V isotherms, confirming the mesoporous nature of the nanocomposites.

Fig. 6.

a Nitrogen adsorption–desorption isotherms, and (b) BJH pore‑size distribution curves of Al_2.66O_3.99/iron oxide nanocomposites synthesized by the co‑precipitation method using different molar ratios of iron and aluminum precursors

The BJH pore-size distribution analysis in Fig. 6b reveals that the pore diameters of the Al_2.66O_3.99/iron oxide nanocomposites are mainly distributed within the 2–12 nm range (average pore size of 11.6, 9.7, 8.1, 10, and 6.1 nm related to C3, C5, C7, C10, and C50, respectively). According to the IUPAC classification, this confirms the mesoporous nature of the synthesized Al_2.66O_3.99/iron oxide nanocomposites. Such mesostructured porosity is commonly observed in alumina-based materials prepared via co-precipitation processes due to the aggregation of nanoscale primary particles and the subsequent formation of interparticle voids.

Also, the specific surface area (SSA) of C3, C5, C7, C10, and C50 samples is 285.6, 265.6, 118.5, 269.7, and 212.65 m²/g, respectively. The SSA comparison of the synthesized Al_2.66O_3.99/iron oxide nanocomposites with bare alumina reported in the scientific literature under the same conditions [21] indicates a decrease in surface area upon incorporation of the iron nitrate precursor. However, the SSAs of the current Al_2.66O_3.99/iron oxide nanocomposites are notably larger than conventional metal oxide nanostructures, thereby underscoring the importance of the engineered nanocomposites. The combination of mesoporous architecture and elevated SSA is exceptionally beneficial for adsorption processes, as it facilitates rapid mass transfer and ensures unhindered diffusion of adsorbates to available active sites.

The textural evolution of the Al_2.66O_3.99/iron oxide nanocomposites is primarily dictated by the interaction between the iron oxide nanoparticles and the alumina framework during the co-precipitation process. The observed reduction in SSA following the incorporation of iron oxide can be chiefly attributed to the deposition of iron oxide species onto the alumina surface and within its porous architecture. This process leads to partial pore blockage and diminishes the accessibility of active adsorption sites during BET measurements. Furthermore, this reduction is likely driven by several synergistic factors: partial surface masking by iron oxide entities, enhanced particle agglomeration, and a slight increase in crystallite size induced by iron integration. Additionally, thermal treatment and iron incorporation may trigger localized structural densification or partial pore collapse. These structural modifications are reflected in the systematic decline of pore volumes (0.83, 0.64, 0.61, 0.45, and 0.32 cm³g^− 1) and the corresponding decrease in SSA values (285.8, 265.5, 118.5, 269.7, and 212.6 m²g^− 1), which all of them collectively limit the overall accessible surface area.

Vibrating sample magnetometry (VSM)

Figure 7a depicts the hysteresis loops of the Al_2.66O_3.99/iron oxide nanocomposites synthesized by the co‑precipitation method using different molar ratios of iron and aluminum precursors, measured at room temperature over a magnetic field range of − 10 to + 10 kOe. The magnetic parameters-saturation magnetization (M_s), coercivity (H_c), and remanent magnetization (M_r)-were calculated from the hysteresis curves and are summarized in Table 4. As shown, increasing the molar ratios of the iron precursor does not lead to a uniform trend in the magnetic parameters’ changes. This non‑uniform behavior can be attributed to lattice and strain effects within the nanocomposites. According to the literature, these parameters can significantly influence magnetic parameters by modifying exchange interactions within the material [38]. For a clearer visualization of the magnetic behavior, the hysteresis loop of sample C50 is presented separately at a higher magnification in Fig. 7b. This sample was selected because it exhibits the highest saturation magnetization (M_s = 0.37 emu g⁻¹) and the lowest coercivity (H_c = 35.22 Oe) among all synthesized nanocomposites, allowing the soft-magnetic characteristics and the narrow hysteresis loop near the origin to be observed more clearly.

Fig. 7.

a Room-temperature magnetic hysteresis loops of the synthesized Al₂.₆₆O₃.₉₉/iron oxide nanocomposites measured in the magnetic field range of − 10 to + 10 kOe. b Enlarged view of the central region of the hysteresis loop for sample C50, highlighting its low coercivity and soft magnetic behavior

Table 4.

Magnetic parameters (Ms, Hc, Mr) of the synthesized Al_2.66O_3.99/iron oxide nanocomposites calculated from VSM measurements

Sample	Ms	Mr	Hc
C3	0.049	0.006	170.56
C5	0.04	0.003	90.63
C7	0.048	0.002	46.6
C10	0.049	0.006	146.93
C50	0.37	0.01	35.22

Based on the hysteresis loops and the extracted parameters, all samples exhibit soft magnetic behavior. Soft magnetic materials rapidly respond to externally applied magnetic fields and exhibit low energy loss during the hysteresis cycle, making them advantageous for magnetic separation in water treatment. Additionally, their relatively high specific surface area further supports their applicability for adsorption‑based pollutant removal. The low saturation magnetization of 0.37 emu g⁻¹ is significantly below the typical range for bulk magnetite (30–70 emu g⁻¹). This value, combined with the aerobic synthesis condition, indicates that the iron-containing phase is not phase-pure magnetite but rather a partially oxidized/poorly crystalline spinel-type iron oxide with possible amorphous content and surface spin-disorder effects.

Recent studies have also highlighted the potential of magnetic and metal oxide-based adsorbents for water treatment [39–43]. Iron oxide is widely incorporated into adsorbent systems because of its magnetic properties and suitability for composite formation and surface modification [40–42]. Combining iron oxide with other functional or metal oxide components can improve the overall functionality of adsorbents while facilitating their magnetic separation and recovery after treatment [39–42]. Moreover, magnetic-field-assisted adsorption has recently attracted attention as a strategy for enhancing adsorption and mass-transfer processes [43]. In this context, the incorporation of iron oxide into the Al₂.₆₆O₃.₉₉ matrix provides magnetic functionality that can facilitate the separation and recovery of the adsorbent from treated water. Thus, the synthesized nanocomposites combine adsorption functionality with magnetic separability, which is advantageous for their potential application in water treatment.

Water treatment

To investigate the effectiveness of nanostructures in water treatment, a stock solution was carefully prepared. For this purpose, some copper sulfate was added to the water, resulting in a copper ion concentration of 46.8 ppm. The mixture was then placed on a magnetic stirrer at room temperature, and 0.050 g of the synthesized samples was added to 50 mL of the aqueous metal-ion solutions. Subsequently, the residual copper concentration was measured at different time intervals, and the removal efficiency was calculated to assess the effect of contact time.

Figure 8 illustrates the pollutant removal efficiency of the nanocomposites at pH = 5.8. As is clear, these nanocomposites exhibit excellent adsorption performance in a very short time toward copper ions in an acidic medium, achieving nearly 94% removal efficiency after only 10 min of contact time.

Fig. 8.

Removal percentage of Cu ions from water using Al_2.66O_3.99/iron oxide nanocomposites synthesized with different iron precursor concentrations

It should be noted that the present adsorption experiments were performed under fixed conditions (initial Cu(II) concentration: 46.8 mg L⁻¹; adsorbent dosage: 1 g L⁻¹; pH: 5.8) to compare the relative adsorption performance of the synthesized Al₂.₆₆O₃.₉₉/iron oxide nanocomposites. Therefore, the reported adsorption capacities should not be interpreted as optimized values across a broader range of operating conditions. Future studies should systematically investigate the effects of solution pH, initial Cu(II) concentration, and adsorbent dosage, including their potential interactions, to establish the optimum operating conditions and equilibrium adsorption behavior of these materials.

To further evaluate the adsorption kinetics of Cu(II), the experimental kinetic data were analyzed using the nonlinear forms of the pseudo-first-order (PFO) and pseudo-second-order (PSO) kinetic models. Nonlinear regression was employed to avoid the potential distortions associated with linear transformation of the kinetic equations and to estimate the kinetic parameters directly from the experimental (q_t)-(t) data. The nonlinear form of the pseudo-first-order model is expressed as Eq. 1 [44]:

1

Where q_t (mg g^− 1) and q_e (mg g^− 1) are the amounts of adsorbate that are adsorbed at any given time, t, and at equilibrium, respectively, and k₁ (min^− 1) is the PFO rate constant.

The nonlinear pseudo-second-order model is expressed as (Eq. 2) [44]:

2

Where k₂ (g mg^− 1 min^− 1) is a rate constant of the pseudo-second-order kinetic model, while q_e and q_t have the same definitions as given above. q_e can be calculated using Eq. 3:

3

In this equation, V(L) is the volume of the Cu(II) contaminated solution, W(g) is the mass of the adsorbent used, C₀ is the initial adsorbate concentration, and C_e is the Cu concentration at the equilibrium. For comparison, the experimental adsorption capacity (q_e,exp), was calculated from the final experimental time point (90 min).

The nonlinear kinetic fits of the Cu(II) adsorption data are presented in Fig. 9. Figure 9a shows the experimental (q_t) values as a function of contact time together with the fitted nonlinear pseudo-first-order (PFO) model, whereas Fig. 9b presents the corresponding nonlinear pseudo-second-order (PSO) fits. In both cases, the models were fitted directly to the experimental data over the contact-time range of 0–90 min.

Fig. 9.

Adsorption kinetics of Cu ions on nanocomposites for the samples using (a) pseudo-first order, and (b) pseudo-second order kinetics (pH 5.8, initial Cu concentration of 46.8 mg L^-1)

The kinetic parameters obtained from the nonlinear fitting of the PFO and PSO models are summarized in Table 5. The experimentally determined adsorption capacity at 90 min, q_e experimental, is also included for comparison with the equilibrium capacities estimated by the kinetic models. For each model, the fitted rate constant (k₁ or k₂) and equilibrium adsorption capacity (q_e) are reported together with their standard errors (SE), coefficient of determination (R²), and chi-square error values.

Table 5.

The calculated q_e, k₁, R²and chi-sqr for pseudo-first-order kinetics, and also q_e, k₂, R²and chi-sqr for pseudo-second-order of water treatment by Al_2.66O_3.99/iron oxide nanocomposites synthesized with different iron precursor concentrations

		Pseudo first order (PFO)				Pseudo second order (PSO)
Sample	qe (exp-90 min) mg g-1	k1 ±SE (min-1)	qe±SE (mg g-1)	R 2	Chi-Sqr	k2 ± SE (g mg-1 min-1)	qe±SE (mg g-1)	R 2	Chi-Sqr
C3	43.97	1.4 ± 0.2	42.32 ± 0.56	0.994	1.61	0.14 ± 0.06	42.7 ± 0.54	0.996	1.16
C5	44.43	1.33 ± 0.31	41.65 ± 0.74	0.990	2.77	0.12 ± 0.06	42.1 ± 0.74	0.992	2.16
C7	43.50	1.6 ± 0.38	41.55 ± 0.51	0.995	1.33	0.25 ± 0.17	41.8 ± 0.52	0.996	1.09
C10	43.42	18.2 ± 0	40.42 ± 0.8	0.986	3.84	0.26 ± 0.34	40.85 ± 0.92	0.988	3.4
C50	42.65	1.06 ± 0.1	41.26 ± 0.44	0.996	0.97	0.07 ± 0.01	41.8 ± 0.33	0.998	0.42

Kinetic analysis provides insight into the rate of Cu(II) uptake and its variation with contact time. For all samples, the pseudo-second-order (PSO) model showed a better statistical fit than the pseudo-first-order (PFO) model, as reflected by its lower 𝜒2 values. However, the superior fit of the PSO model alone does not constitute conclusive evidence of chemisorption, nor does it definitively establish the underlying adsorption mechanism [45].

To further evaluate the adsorption performance of the synthesized nanocomposites, the experimentally observed Cu(II) adsorption capacity was compared with those reported for selected Cu(II) adsorbents in the literature (Table 6). Among the investigated samples, C5 exhibited the highest adsorption capacity of 44.31 mg g⁻¹ after 90 min under the applied experimental conditions. As shown in Table 6, this value is comparable to those reported for previously studied Cu(II) adsorbents under similar batch adsorption conditions, indicating the competitive adsorption performance of the Al₂.₆₆O₃.₉₉/iron oxide nanocomposite.

Table 6.

Comparison of the Cu(II) adsorption capacities of the present Al₂.₆₆O₃.₉₉/iron oxide nanocomposite (sample C5) with previously reported adsorbents under comparable batch conditions

Adsorbent	Cu(II) initial concentration (mg/L)	Adsorbent dose (g/L)	pH	Contact time (min)	Experimental adsorption capacity (mg/g)	Ref.
Al₂.₆₆O₃.₉₉/Fe 3 O 4 (C5)	46.8	1.0	5.8	90	44.31	this work
Magnetite–chitosan composite	50	1.0	5.0	90	24.25	[46]
Wool-keratin modified Fe 3 O 4	40	1.0	5.0	90	27.4	[47]
Hazelnut-shell activated carbon	50	3	6	90	36.3	[48]
Activated Eupatorium adenophorum (AEA)	50	1.0	5.0	90	37.005	[49]

Further insight into the adsorption mechanism can be obtained from the changes in surface functional groups and the characteristics of the adsorbed Cu species. Accordingly, FTIR, EDX, and elemental-mapping analyses were performed after Cu(II) adsorption, and the corresponding results are discussed below.

The FESEM images in Fig. 10 show noticeable morphological changes in the synthesized Al_2.66O_3.99/iron oxide nanocomposites (except the C50 sample) after Cu adsorption, compared with the samples before Cu removal, indicating effective interaction between the adsorbent surface and Cu ions. To confirm the trapping of copper ions in the nanocomposites, the samples were analyzed, and the bar chart of elemental atomic percentages (obtained from EDX analysis) is shown in Fig. 10. The results indicate that a significant amount of copper (0.74–1.57) is trapped in the samples, confirming the high water treatment efficiency of this sample. Figure 10f presents the bar chart of the atomic percentages of elemental composition obtained from the EDX spectra of the C3, C5, C7, C10, and C50 samples after treatment with acidic water containing copper ions. As can be clearly observed, a considerable amount of copper is detected in all samples, confirming the successful adsorption of Cu ions onto the synthesized adsorbents. The elemental mapping (MAP) analysis shown in Table 7 demonstrates a relatively uniform spatial distribution of Cu throughout the synthesized Al_2.66O_3.99/iron oxide nanocomposites after the water treatments. This observation confirms the effective immobilization of copper ions on the adsorbent surface and suggests the presence of well-dispersed active adsorption sites without significant localized agglomeration. The uniform dispersion of Cu signals suggests that the adsorption process occurred effectively across the available active sites rather than being localized in isolated regions, confirming the favorable adsorption performance of the synthesized nanocomposites toward Cu ions.

Fig. 10.

FESEM images of the samples after treating the acidic water containing copper ions with the (a) C3, b C5, c C7, d C10, e C50 samples, and (f) the bar chart of the atomic percentages of elemental composition obtained from EDX spectra after treating the acidic water

Table 7.

MAP images of copper adsorbed on the Al_2.66O_3.99/iron oxide nanocomposites through treating the acidic water containing copper ions with the C3, C5, C7, C10, and C50 samples

To recognize the chemical bond changes induced by copper adsorption, Fourier transform infrared (FTIR) spectroscopy was carried out on these Al_2.66O_3.99/iron oxide nanocomposites used in the water treatments, and the spectrum is presented in Fig. 11a. For comparison, the FTIR spectrum of the samples before water treatment is also shown in Fig. 11b. The enhancement of the absorption band around 1093 cm^{− 1} after Cu(II) adsorption may indicate alterations in the surface chemical environment due to complexation interactions between Cu(II) ions and hydroxyl-containing active sites of the nanocomposite. Comparable FTIR spectral changes associated with surface complexation and hydroxyl group involvement have been reported in previous studies on metal oxide adsorbents [50]. The modification of this region suggests the involvement of surface functional groups in the adsorption process. Furthermore, the observed shifts in the wavelength of the FTIR absorption peaks related to Al and O bonds could be due to the trapped Cu ions inside the alumina/iron oxide nanocomposites during the treatment process, which was confirmed by EDX and Map analyses. The existence of a broad absorption peak in the range of 500 to 1000 cm^{− 1} indicates asymmetric stretching vibrations of Al-O-Al bonds [51]. The positions of the absorption bands in this region serve as a fingerprint for distinguishing the chemical bonds and crystalline phases present in metal oxide systems, and in the current samples they are mainly associated with Al–O and Al–O–Fe linkages [52]. γ-alumina demonstrates two well-known FT-IR absorption peaks at approximately 800–850 cm^{− 1} and 550–600 cm^{− 1}, which can be observed in Fig. 11a and b, confirming the formation of the γ‑alumina phase in the composites [21, 53, 54]. The absorption band observed at 500–550 cm⁻¹ corresponds to the Fe–O stretching vibrational mode characteristic of iron oxide. In addition, the peak appearing at 600–650 cm⁻¹ is attributed to the Fe–O bending vibrational mode in iron oxide [55].

Fig. 11.

Fourier transform infrared (FTIR) analyses of the synthesized Al_2.66O_3.99/iron oxide nanocomposites (a) after, and (b) before water treatment

The peaks present at 1600–1650 cm⁻¹ and around 3500 cm⁻¹, respectively, confirm the presence of the O–H bending vibrational mode and the –OH stretching vibrational mode of water molecules [56]. The formation of bonds correlated with alumina and iron oxide is thus substantiated by the FTIR spectroscopy results [57].

Interfacial interactions and Cu(II) adsorption

In aqueous solution, hydroxyl groups present on alumina and iron-oxide surfaces can interact with dissolved Cu(II) ions through their surface oxygen sites. The protonation state of these groups depends on the solution pH and can therefore influence the surface charge and the interaction of Cu(II) with the adsorbent [58, 59]. The changes observed in the FTIR spectra after Cu(II) adsorption are consistent with a change in the chemical environment of these surface oxygen-containing groups. The presence of two oxide phases may further modify the chemical environment of these surface sites. At the alumina/iron-oxide interface, oxygen atoms can be influenced by the neighboring metal centers, resulting in surface environments that differ from those of the individual oxides. Such interfacial heterogeneity can provide different types of adsorption sites for Cu(II). In addition, the variation of the optical band-gap values with iron content suggests that incorporation of the iron-containing phase modifies the electronic environment of the composite, which may also affect the reactivity of interfacial oxygen species [60]. However, the present results do not provide direct evidence for a specific charge-transfer pathway or band alignment; therefore, these effects are considered possible contributions to the adsorption behavior rather than definitive mechanistic assignments.

Conclusion

Alumina/iron oxide (the Al_2.66O_3.99/iron oxide) nanocomposites with varying phase compositions were successfully synthesized using a straightforward and cost-effective co-precipitation approach. XRD and Rietveld refinement analyses were used to confirm the formation of Al₂.₆₆O₃.₉₉ and iron oxide phases. The iron oxide phase fraction was found to be strongly dependent on the precursor composition. A thorough characterization of the nanocomposites revealed that their structural, optical, morphological, and magnetic properties were substantially influenced by the quantity of iron oxide present. Magnetic measurements revealed soft magnetic behavior for all samples, while the highest magnetic response was obtained for the C50 sample due to its larger iron oxide fraction.

The synthesized nanocomposites demonstrated noteworthy adsorption capabilities towards Cu(II) ions in acidic aqueous media. A rapid adsorption process was observed during the initial minutes of contact time, and the majority of samples achieved removal efficiencies above 90% after 90 min. The adsorption of Cu(II) ions was further supported by the noticeable morphological changes observed in FESEM images together with the appearance of Cu in EDX spectra. Moreover, the modification of FTIR bands, especially in the region of , suggests the participation of surface functional groups in the adsorption process. The adsorption kinetics were generally well described by the pseudo-second-order model, based on the nonlinear fitting and statistical evaluation of the kinetic data. However, the kinetic model itself was not used to assign a specific adsorption mechanism. Instead, the adsorption behavior was further evaluated using complementary post-adsorption characterization to identify the surface interactions involved in Cu(II) removal. The results obtained demonstrate that the prepared alumina/iron oxide nanocomposites combine simple synthesis, tunable physicochemical properties, and efficient adsorption capability, making them promising candidates for wastewater treatment and environmental remediation applications. Future studies should include systematic equilibrium experiments over a broad range of initial Cu(II) concentrations under controlled pH and temperature conditions to construct reliable adsorption isotherms. Fitting the resulting equilibrium data to appropriate nonlinear isotherm models, such as the Langmuir and Freundlich models, would provide a more comprehensive evaluation of the adsorption capacity, surface heterogeneity, and adsorbate–adsorbent interactions of the Al₂.₆₆O₃.₉₉/iron oxide nanocomposites.

Author contributions

ZG and MA did Conceptualization and methodology; ZG applied software, MA investigated validation, and ZG did the formal analysis. ZG, MA, and MJA did the investigation and data curation; ZG wrote the initial draft of the manuscript, and MA and MJA did the review and editing. All authors have read and agreed to the published version of the manuscript. Also, all authors reviewed the manuscript.

Funding

This research did not receive any specific grant from funding agencies.

Data availability

All data included in this paper are available upon request by contacting the corresponding author.

Declarations

Ethical approval

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Competing interests

The authors declare no competing interests.