Enhanced adsorption of an organic dye by phyto-synthesized CuO nanoparticles derived from Malva sylvestris for sustainable environmental remediation

Abstract

This study presents the green synthesis of copper oxide (CuO) nanoparticles using Malva sylvestris extract as a biogenic reducing and stabilizing agent, offering an eco-friendly and sustainable route to nanoparticle production. The synthesized CuO nanoparticles were thoroughly characterized through advanced analytical techniques to elucidate their morphology, crystalline structure, and surface properties. These nanoparticles were subsequently employed as an efficient adsorbent for the removal of Congo red dye from aqueous solutions. The adsorption process was systematically optimized by investigating the effects of pH, contact time, adsorbent dosage, and initial dye concentration. Kinetic studies indicated that the adsorption followed a pseudo-second-order model (R² = 0.9997), suggesting that chemisorption was the predominant mechanism. Equilibrium data showed excellent correlation with both Langmuir and Freundlich isotherm models (R² > 0.99), implying the coexistence of monolayer and multilayer adsorption processes. Thermodynamic parameters revealed that the adsorption was spontaneous and exothermic (ΔH° = –34.35 kJ mol⁻¹), accompanied by reduction in entropy (ΔS° = –106.46 J mol⁻¹ K⁻¹), supporting a combination of physical and chemical adsorption mechanisms. The CuO nanoparticles demonstrated a maximum adsorption capacity of 6.99 mg g⁻¹ for Congo red under optimized conditions: 4 ppm dye concentration, pH 7.0, temperature 20 °C, and 0.02 g adsorbent per 10 mL solution, achieving equilibrium within 30 minutes. The CuO nanoparticles retained over 78% of their adsorption efficiency after four regeneration cycles, highlighting their reusability and potential for long-term applications. Overall, the findings underscore the significant adsorption capability and environmental compatibility of phyto-synthesized CuO nanoparticles, positioning them as a promising candidate for sustainable wastewater treatment technologies.

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1. Introduction

Nanoparticles with grain sizes less than 100 nm are now attracting a lot of attention in a number of different sectors. This is because of the significance of their physico-chemical characteristics in comparison to the bulk materials. These characteristics include, for instance, tunable bandgap energies, which enhance optical and electronic properties, zeta potential, which governs colloidal stability, and interfacial interactions, factors that are critical in applications such as adsorption, catalysis, and biomedicine.¹ Nanoparticles possess several applications; this is attributed to their unique features; they also vary in size, shape, and dimensions, as well as in their composition.²

Many nanomaterials, such as nanocomposites, carbon-based nanomaterials, and metal–organic frameworks (MOFs), have been prepared for various applications in fields such as energy, sensing, environment, and biomedicine.^3–7 Many metallic elements have a tendency to react with oxygen under a wide range of circumstances, resulting in the formation of metal oxides with various structural configurations. Metal oxide nanoparticles (MONPs) are nanoscale particles consisting of metal atoms linked to oxygen atoms, exhibiting extraordinary properties owing to their unique compositional structure. The structure of MONPs can vary, predominantly exhibiting crystal structures such as cubic or tetragonal shaped structures. MONPs can crystallize in various polymorphic forms, with cubic and monoclinic structures being among the most common. These structural variations strongly influence their surface properties and functional behavior. Cubic oxides generally exhibit high symmetry and dense packing, which contributes to better thermal stability but may limit the availability of active surface sites for adsorption or catalysis. On the other hand, monoclinic oxides possess lower symmetry and more distorted coordination environments, often resulting in enhanced surface reactivity, greater defect density, and improved adsorption capacity. These differences in crystal geometry directly impact their performance in applications such as environmental remediation and heterogeneous catalysis.^8,9

Their morphology plays a vital role in influencing functioning and reactivity for diverse applications.^10,11 The effects of these nanostructures on medicine, chemistry, physics, information technology, energy storage, catalysis, sensing, and materials science have prompted research into their synthesis. MONPs and similar nanocomposites have been studied extensively for their characteristics and applications; characteristics such as controllable morphologies, distinctive crystalline nature, flexible surface chemistry, high stability, specific catalytic activity, and customizable band edges make MONPs effective instruments for many different applications.^11–14

Nowadays, transition metal oxide nanoparticles are the most commonly utilized nanoparticles; for instance, the oxides of iron, nickel, zinc, and copper occupy an extremely important place in different fields.^15–18 Cupric oxide (CuO), among transition metal oxides, is noteworthy for its distinctive properties and has been utilized in various applications, including gas sensors,¹⁹ biosensors,²⁰ catalysis,²¹ wastewater treatment, batteries and energetic materials.²² The extensive use of this material stems from its notable attributes, including stability, cost-effectiveness, plenteousness, hydrophilicity, nontoxicity, and the straightforward synthesis of diverse nanoparticles in varied sizes and forms (e.g., floral, spherical, and nanorods).^23,24

The three primary prerequisites for nanoparticle production are the selection of a green or environmentally friendly solvent (e.g., water or ethanol), a non-toxic stabilizing substance (such as plant polysaccharides or starch), and an efficient reducing agent (commonly found in plant extracts, like flavonoids, polyphenols, or ascorbic acid).²⁵

Various synthetic methods, including chemical methods, physical methods, and biosynthetic approaches, have been widely used for nanoparticle creation. Chemical and physical synthesis methods are often unsafe, technically demanding, hard, and expensive and involve the use of dangerous and harmful chemicals, which cause significant environmental risks.^26,27 The green synthesis pathway is a safe, biocompatible, and environmentally sustainable method for synthesizing nanoparticles utilizing plants and microbes for many applications.^28,29 The biosynthesis can be carried out using bacteria,³⁰ algae,³¹ fungi³² and plants. Different plant parts such as seeds, roots, stems, fruits, and leaves have been extensively employed for the synthesis of diverse nanomaterials. This is largely attributed to the presence of abundant phytochemicals in plant extracts, which serve as both reducing and stabilizing agents during nanoparticle formation.^33,34

Plant-derived phytochemicals play a crucial and elegant role in the green synthesis of nanoparticles. Compounds such as flavonoids, phenolic acids, tannins, and terpenoids naturally present in leaves and other plant parts help reduce metal ions by acting as electron donors. At the same time, these molecules gently wrap around the newly formed nanoparticles, stabilizing them and preventing unwanted clumping. This dual function, reduction and stabilization, makes plant extracts powerful tools for producing nanoparticles in a simple, eco-friendly, and efficient manner.^35,36 Therefore, it is of great importance to use a green, low-cost route for production of the desired nanoparticle.

Freshwater is a vital component for human existence. Nonetheless, the sources of clean water are contaminated because of increasing worldwide population, urbanization, and industrialization, accompanied by many forms of domestic and industrial wastes.³⁷ Recently, wastewater polluted with artificial dyes has attracted considerable global interest due to the persistence and limited biodegradability of these dyes, posing possible environmental and health risks arising from their carcinogenic, mutagenic, and toxic properties.³⁸ Moreover, these dyes obstruct the penetration of sunlight, adversely affecting the process of photosynthesis and development of plants, elevating chemical and biological oxygen requirement and thus degrading the aesthetic qualities of aquatic ecosystems.³⁹ Adding to that, these dyes may aggravate respiratory conditions and induce skin irritation.⁴⁰ The enduring existence of these dyes in the environment demands the development of novel treatment strategies for their removal from aquatic systems.⁴¹ Thus, the pressing need for creative, environmentally sustainable, economical, and enduring wastewater treatment solutions has become vital for guaranteeing the sustained supply of fresh water.

Among different methods for dye treatment, the techniques based on adsorption are extensively recognized.⁴² Lately, researchers working on wastewater treatment have redirected their attention towards adsorbent materials that prioritize sustainability, quick adsorption rates, economic viability, and superior adsorptive efficacy.⁴³ A number of significant materials, including MONPs, carbon nanomaterials, activated carbon,⁴⁴ natural minerals,⁴⁵ and MOFs,^46,47 have been widely investigated for dye remediation in contaminated water.

Congo red (CR) is a synthetic azo dye widely used in the textile and printing industries. It is environmentally persistent and resistant to biodegradation, leading to long-term contamination of water bodies. Beyond its ecological impact, CR poses serious health risks due to its potential mutagenic and carcinogenic effects. Its metabolic byproducts, including benzidine, are known to be toxic and have been linked to cancer in humans. For these reasons, CR is classified as a hazardous substance by environmental authorities, and its removal from wastewater is considered a priority for environmental remediation.^48,49

M. sylvestris has been selected in this research for its bioactive chemicals, which reduce and stabilize nanoparticles in green manufacturing. Accessibility, biocompatibility, and therapeutic qualities enable its usage in sustainable nanotechnology.⁵⁰

Despite various studies on CuO nanoparticles for dye removal, limited research has focused on using Malva sylvestris leaf extract as a green, bioactive precursor for nanoparticle synthesis. This plant offers unique phytochemicals that may enhance particle morphology and adsorption performance.⁵¹ Moreover, our integrated evaluation of adsorption behavior with kinetic, isotherm, and thermodynamic modeling, coupled with regeneration analysis, distinguishes this work as a comprehensive and eco-conscious approach to dye remediation.

Recent studies have explored functionalized nanomaterials for dye removal. For instance, Debnath and Sinha developed polymer-based ZnO and hematite–PANI nanocomposites for dye removal, employing ultrasound-assisted synthesis and emphasizing adsorption kinetics and cost evaluation.^52,53 In contrast, our study presents the green synthesis of CuO nanoparticles using Malva sylvestris extract, offering a unique phytochemical approach. Furthermore, we combine adsorption modeling with thermodynamic analysis and regeneration assessment, providing a more environmentally sustainable and functionally comprehensive strategy for dye remediation.

Hence, the aim of our research is to synthesize CuO nanoparticles using an effective green synthesis method involving the M. sylvestris leaf extract. The synthesized CuO nanoparticles are evaluated as an adsorptive nanomaterial for removing CR dye from water, alongside modeling experimental outcomes using isotherm and kinetic models and thermodynamic studies. The nanoparticle is finally regenerated and reused against the CR dye to show its stability and efficiency.

2. Experimental part

2.1. Materials

All chemicals employed were of analytical grade and used without further purification. Copper nitrate tri-hydrate (Cu(NO₃)₂3H₂O, 99%), Congo red (CR) dye, sodium hydroxide (99%), and hydrochloric acid (37%) have been acquired from Merck.

2.2. Characterization

Optical absorption was measured using UV-visible absorption spectra in the range 290–650 nm; the spectra were measured via single-beam spectroscopy (EMCLAB GmbH GERMANY). X-ray diffraction (XRD) was utilized to examine material crystallinity, structure and phase purity. The analysis was performed utilizing a PAN analytical X′Pert PRO, scanning at 1° min⁻¹ throughout a 2θ range of (5° to 75°), utilizing radiation of Cu Kα (λ = 1.540 Å). The chemical structure of the resultant nanostructures was examined with an EDX inside the SEM system. EDX reliant on the interactions among the sample and X-ray excitation is utilized for chemical and elemental analyses. The size and surface appearance of the synthesized nanoparticles have been examined using field emission scanning electron microscopy (FE-SEM). The surface characteristics and purity of nanoparticles were evaluated by obtaining Fourier transform infrared (FT-IR) spectra utilizing a PerkinElmer (RX1) FT-IR spectrometer within a range of 400–4000 cm⁻¹. The zeta potential technique (Zetasizer Nano ZS, Malvern) has been utilized to determine and identify the stability of CuO nanoparticles. The specific surface area (SSA) of the produced CuO nanoparticles, following calcination, ultrasonic treatment, and subsequent air drying, has been determined using a 5 point N₂ adsorption isotherm by BET analysis, measured using a Micromeritics TriStar II Plus equipped with Microactive software and the Microtrac Bel Cor BElSORP-Mini. Prior to the analysis, the sample powder underwent drying for 4 hours at 150 °C in synthetic air.

2.3. Plant collection

M. sylvestris leaves were plucked and collected from the Bira kapra village-Zebar area/Duhok–Kurdistan region of Iraq during the Month of March 2024. All the solutions were produced with deionized water.

2.4. M. sylvestris leaf extract preparation

M. sylvestris leaves were thoroughly rinsed and washed using deionized water multiple times to eliminate contaminants and dust particles, followed by air drying at room temperature in a dark environment. 10 grams of the dried leaves were finely powdered and mixed in 100 mL of deionized water. The solution was then further heated for one hour at 75 °C with stirring. After that, the extract was filtered and centrifuged to proceed with the next steps.

2.5. Phytochemical screening

To carry out the phytochemical investigation of the plant extract, several chemical tests were performed for testing the extract, according to the following standards.^54–61

2.6. CuO nanoparticle synthesis

The fabrication process of CuO nanoparticles was conducted utilizing a precipitation approach based on the previous methods with minor modifications.⁶² In a standard procedure, a stoichiometric amount of copper ion precursor (0.1 mol L⁻¹) is mixed with 100 mL of deionized water, and then the solution is heated under magnetic stirring at 65 °C; after 30 min, the plant extract of M. sylvestris (20 mL) is added dropwise to the solution and further heated for 30 min; the solution color changes from blue to brown-green. At this temperature, an aqueous solution of sodium hydroxide (NaOH, 1 mol L⁻¹) was dropwise added until the pH reached 12; a suspended precipitate of nanoparticles was formed; the solution was stirred continuously for 1 hour. The precipitate was then centrifuged, washed and rinsed many times with absolute solvent of ethanol and deionized water. The precipitate was oven-dried for 12 hours to remove moisture and residual plant extract. To promote oxide formation and enhance crystallinity, the dried sample was then calcined at 450 °C for 2 hours at a heating rate of 4 °C min⁻¹.

2.7. Batch adsorption experiments

Comparative research was done to assess the effectiveness of synthesized CuO nanoparticles for CR dye removal from aqueous solutions. Batch experiments have been performed to determine and evaluate the impacts of different variables, including initial CR dye concentration (ranging from 4 to 12 mg L⁻¹), pH (4, 5, 6, 7, 9, and 12), contact time (5–60 min), temperature (20, 25, 30, 35, and 40 °C), and adsorbent dose (0.01–0.03 g). For each experiment, a 100 mL Erlenmeyer flask was employed, containing 30 mL of CR solution with specified concentrations, pH levels, temperatures, adsorbent doses, and contact times. The solution was then stirred consistently at a rate of 300 rpm using a digital magnetic hotplate stirrer. At regular intervals samples were collected, centrifuged for 2 min at 8000 rpm, and the absorbance of the supernatant was checked and measured. The adsorption capacity was then subsequently determined via the formula, q_e:

The ultimate and initial concentrations of the CR dye in the solution (C_e and C₀, respectively) are expressed in (mg L⁻¹) and are used to determine solution volume (V) in liters and mass (m) of adsorbents in grams.⁶³

Experimental equilibrium data have been analyzed utilizing the models of Freundlich and Langmuir isotherms. The Langmuir model is articulated as shown in the following equations:^64–66

R_L = 1/(1 + bC₀) (3)

In this context, q_max is the maximum adsorption (mg g⁻¹), q_e represents adsorbate solid phase concentration at equilibrium, b denotes the Langmuir constant (L mg⁻¹), which corrosponds to the adsorption energy, and C_e denotes the concentration equilibrium of the CR dye (mg L⁻¹). The maximum adsorption and Langmuir constant might be acquired by graphing C_e/q_e against C_e, with the intercept representing 1/bq_max. The R_L value denotes the isotherm shape as unfavorable (R_L > 1), favorable (0 < R_L < 1), linear (R_L = 1), or irreversible (R_L = 0).⁶⁷

The isotherm model of Freundlich is an empirical equation utilized for heterogeneous surfaces or surfaces with varying affinities. The logarithmic representation of the isotherm model of Freundlich is articulated as:⁶⁸

where K_f and n indicate the adsorption capacity and intensity, with K_f serving as a constant for adsorption efficiency, and 1/n is the slope which varies from zero to one; this reflects the degree of surface heterogeneity, where values approaching zero suggest more heterogeneity. A ratio of 1/n less than 1 implies a typical Langmuir isotherm, where a value larger than 1 signifies cooperative adsorption. K_f and n may be ascertained by graphing ln q_evs. ln C_e.

The adsorption kinetics were examined utilizing the models: pseudo-first order and pseudo-second order. The pseudo-first order model is articulated using the Lagergren equation, which determines the rate of the adsorption constant (k₁) from the plot of ln(q_e − q_t) vs. t, in which q_t and q_e indicate the adsorbed quantities at time t and at equilibrium.⁶⁹

ln(q_e − q_t) = ln q_e − k₁t (5)

The pseudo-second order model, on the other hand, calculates the second order rate constant (k₂) from the linear plot of t/q_tvs. t.The intra-particle diffusion model calculates the diffusion rate constant (k_id) from the linear plot of (q_t) versus (√t), based on the Weber–Morris equation:⁷⁰

q_t = k_id·t^1/2 + C (7)

Thermodynamic parameters, involving entropy (ΔS°), enthalpy (ΔH°), and free energy (ΔG°), were computed utilizing equations that incorporate the equilibrium constant (K_c), absolute temperature (T), and the universal gas constant (R). The parameters were derived using the ln K_c against 1/T plot, with the slope indicating DH°/R and the intercept denoting DS°/R.⁷¹

ΔG° = −RT ln K_c (9)

2.8. pH at the point of zero charge (pHpzc)

The point of zero charge (pHpzc) is defined as the pH at which the surface of the adsorbent carries no net electrical charge, meaning the concentrations of positive and negative surface charges are equal. The pHpzc of CuO nanoparticles was determined using the pH drift method. A series of 0.005 mol L⁻¹ NaCl solutions were adjusted to initial pH values ranging from 2 to 12 using either HCl or NaOH (0.1 M). Then, 0.03 g of CuO nanoparticles was added to 20 mL of each solution in Erlenmeyer flasks and allowed to equilibrate at room temperature for 48 hours. After stabilization, the final pH of each solution was then recorded. The difference between the initial and final pH values (eqn (11)) was plotted against the initial pH. The pHpzc was identified as the point where ΔpH equals zero, in other words, where the curve intersects the x-axis.^72,73

ΔpH = pH_f − pH_i (11)

2.9. Adsorbent regeneration process

Practical adsorption requires an adsorbent's ability to be renewed and reused with optimal efficiency. To test the feasibility of regenerating CuO nanoparticles, 50 mL of the CR dye with 4 ppm concentration was brought into contact with 0.1 g for half-an-hour time duration at 20 °C temperature and pH 7.0.” to “50 mL of the CR dye with 4 ppm concentration was mixed with 0.1 g of CuO nanoparticles for half-an-hour at 20 °C and pH 7.0. UV-vis spectroscopy was utilized to quantify the adsorbed CR dye. The adsorbent was washed with 50% ethanol (v/v) solution in deionized water five times and tested for CR dye concentration utilizing a UV-vis spectrophotometer. Five adsorption–desorption cycles were done. The following equation was used to calculate the regeneration percentage (Re%).⁷⁴

3. Results and discussion

3.1. Phytochemical analysis of the M. sylvestris leaf extract

An examination of phytochemicals was carried out to determine the bioactive components found in M. sylvestris leaves using several chemical tests as shown in Fig. 1 and to understand the relationship among these phytochemicals and their biological activity, which is crucial, particularly for applications such as green synthesis of nanoparticles. The extract was essential for reducing and stabilizing the nanoparticles. Polyphenolic metabolites (flavonoids, phenolics, and tannins) in the M. sylvestris extract are suggested to serve as primary reducing agents for Cu²⁺ ions through hydroxyl and carbonyl functional groups, whilst proteins, amino acids, and saponins are anticipated to function as capping and stabilizing agents. This cooperative approach facilitates both the nucleation and stability of CuO nanoparticles, in accordance with previous research on plant-mediated green synthesis.^75,76 The principal bioactive components discovered in the extract are summarized in Table 1. These results closely align with current research,⁵⁰ further affirming the consistency and potential of the M. sylvestris plant in environmentally sustainable nanotechnology applications.

Fig. 1 Image showing the phytochemical test screening results.

Table 1 Phytochemical test results

Active constituent	Chemical test	Results	Indication	Ref.
Flavonoid	Lead acetate	++ve	Yellow ppt	54
Carbohydrate	Molish	+ve	Violet ring	55
Alkaloid	Hager	−ve	No change	56
Phenolic	Lead acetate	++ve	Yellow ppt	57
Glycoside	Benedict	+ve	Reddish ppt	58
Tannin	Braymer	+ve	Brownish-green	59
Protein	Biuret	+ve	Purple color	60
Amino acid	Ninhydrin	+ve	Violet green color	60
Saponin	Foam	+ve	Foam formation	61

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3.2. Characterization of CuO nanoparticles

The morphological characteristics of synthesized CuO nanoparticles have been assessed utilizing FE-SEM. The nanoparticles have a granular and distinct spherical shape, as seen in the FE SEM picture in Fig. 2A and B. The particles seem to be uniformly distributed with little aggregation, which is prevalent in nanoparticles owing to abundant surface energy, electrostatic attraction and also the incorporation of biological components in the process of synthesis. However, it should be noted that some degree of aggregation observed in the SEM images may also result from the sample drying process during SEM preparation.^77,78 The uniform size of nanoparticles ranges from 26 nm to 41 nm, indicating good control of parameters over the process of synthesis. This nanoscale uniformity and spherical shape align with typical results of green synthesis, where plant based reducing agents affect particle nucleation and stabilization.⁷⁹

Fig. 2 (A) and (B) FE-SEM images of CuO NPs. (C) and (D) EDX spectrum and mapping spectrum of CuO NPs. (E) XRD pattern of the CuO NPs. (F) FTIR spectra of the Malva sylvestris extract and synthesized CuO NPs. (G) UV-visible spectrum of the plant extract and CuO NPs. (H) Zeta potential analysis of CuO NPs. (I) and (J) BET analysis of CuO NPs.

The EDX mapping and spectrum validate the effective production of synthesized CuO nanoparticles, with some additional elements probably introduced as contaminants during sample preparation or analysis. Fig. 2C shows strong peaks belonging to the copper element and oxygen. Quantitative analysis reveals the atomic percentages of Cu of approximately 26.2% and O around 46.3, consistent with expected stoichiometry and signifying the synthesis of CuO nanoparticles. Carbon was present in minimal quantities as a result of using the extract of M. sylvestris leaves in the synthesis process. A comparable spectrum is seen for the Calotrupis G. plant leaf extract⁸⁰ and Moringa oleifera leaf extract⁸¹ The EDX spectrum was verified using the EDX mapping figure, which is essential for ensuring consistent material properties. As shown in Fig. 2D, the uniformity distribution signifies the successful synthesis of CuO nanoparticles.

The XRD spectrum of synthesized CuO nanoparticles reveals a series of diffraction peaks, verifying their crystalline structure. The identified peaks align with the monoclinic CuO phase (JCPDS 96-110-0029).^82,83 Significant peaks appeared at 2θ values of 32.69°, 35.75°, 38.92°, and 49.06°, corresponding to the 110, −111, 111, and −202 planes, respectively, Fig. 2E. The pattern in the figure shows diffraction peaks of the synthesized nanoparticles and their corresponding crystalline phases; the red curve represents the XRD pattern of the CuO nanoparticle, whereas the overlaid lines correspond to reference diffraction patterns for phase identification. The peak distribution and positions match well with monoclinic CuO (tenorite) as indicated by the blue reference line peak, confirming the successful synthesis of the expected crystalline phase. Lattice parameters for biosynthesized nanoparticles were a = 4.653, b = 3.410, and c = 5.108. These findings align with prior publications on CuO nanoparticles synthesis.^83–85 The average crystallite size of the biosynthesized CuO nanoparticles, calculated from the Debye–Scherrer equation applied to the main diffraction peaks, was ∼17.7 nm (Table 2). This value is smaller than the particle size observed in FE-SEM images (26–41 nm). Such a difference is commonly reported in nanomaterials studies, since XRD estimates the size of individual crystallites, whereas SEM reflects the overall particle size, which may consist of multiple crystallites.⁸⁶

Table 2 XRD peak parameters, FWHM, and crystallite size of CuO nanoparticles calculated using the Scherrer equation

No.	hkl	2θ (°)	FWHM (°2θ)	B (°)	cos(θ)	β (radians)	D (Å)
1	110	32.7	0.5486	0.0096	0.9595	0.0096	15.10
2	−111	35.7	0.4131	0.0072	0.9518	0.0072	20.22
3	111	38.9	0.51	0.0089	0.9429	0.0089	16.53
4	−202	49.1	0.477	0.0083	0.9095	0.0083	18.32
5	020	53.7	0.5865	0.0102	0.8921	0.0102	15.19
6	202	58.7	0.4879	0.0085	0.8715	0.0085	18.70
7	−113	61.9	0.4193	0.0073	0.8575	0.0073	22.11
8	022	66.1	0.6897	0.0120	0.8381	0.0120	13.75
9	−311	66.7	0.4385	0.0076	0.8352	0.0077	21.70
10	113	68.5	0.6486	0.0113	0.8265	0.0113	14.83

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The FTIR spectra of M. sylvestris leaf extract and synthesized CuO nanoparticles are presented in Fig. 2F, revealing characteristic bands that confirm the presence of biomolecules involved in nanoparticle formation and surface functionalization.⁸⁷ In the plant extract spectrum, a broad band at 3402 cm⁻¹ is attributed to O–H stretching vibrations, indicating hydroxyl groups from polyphenols or alcohols. Peaks at 2922 cm⁻¹ and 2852 cm⁻¹ are assigned to asymmetric and symmetric C–H stretching of aliphatic –CH₂ groups, typically found in fatty acids and phytosterols.⁸⁸

The fingerprint region shows absorptions at 1642 cm⁻¹ (C=C stretching in aromatic rings), 1385 cm⁻¹ (O–H bending), and 1250 cm⁻¹ (C–O stretching), which are consistent with flavonoids and phenolic compounds. Additional bands at 1102 cm⁻¹ and 617 cm⁻¹ correspond to C–O–C and C–H bending, respectively, further supporting the presence of secondary metabolites like tannins and glycosides, which likely acted as reducing and stabilizing agents during synthesis.^88,89

For the CuO nanoparticles, a prominent peak at 438 cm⁻¹ confirms the formation of Cu–O bonds, characteristic of CuO lattice vibrations. In addition, weaker absorptions at 1409 cm⁻¹ (N–H bending/C–N stretching or symmetric COO⁻ stretching), 1028 cm⁻¹ (C–O–C/C–O stretching of glycosidic or phenolic residues), and 871 cm⁻¹ (aromatic C–H out-of-plane bending) indicate that plant-derived functional groups remain attached to the nanoparticle surface. These residual biomolecular features suggest the presence of a thin phytochemical capping layer that enhances colloidal stability and adsorption activity. Such assignments are consistent with earlier reports on CuO nanoparticles synthesized using plant extracts.^90–92 The FTIR spectra further confirm the presence of phytochemical residues (–OH, –C=O, and –NH₂ groups) on the CuO nanoparticle surface, derived from the M. sylvestris extract. These biomolecules can provide additional adsorption-active moieties, enhancing dye interaction through hydrogen bonding, electrostatic interactions, and π–π stacking, thereby increasing adsorption capacity compared to chemically synthesized CuO.^93,94 Although calcination at 450 °C removes most of the plant extract, the EDX and FTIR spectra indicate that only a minor fraction of thermally stable phytochemical residues remains on the CuO surface, providing additional functional groups that contribute to dye adsorption.

The optical characteristics of CuO nanoparticles are examined using UV-visible spectroscopy. The conversion of the copper nitrate precursor to CuO nanoparticles was verified by analyzing the UV-vis spectra between 250 and 600 nm for both the plant extract and the resultant product as shown in Fig. 2G. As anticipated, CuO nanoparticles exhibit a peak within the range of 400 to 500 nm, namely at 442 nm, attributable to the characteristic absorption of nanoparticles, which can be easily distinguished from the spectrum of the plant extract.⁹⁵

The stability and dispersion of CuO nanoparticles have been investigated using the zeta potential technique; as shown in Fig. 2H, the average zeta potential value is −25 mv, indicating their good colloidal characteristics, enhanced disparity and prolonged stability.⁸¹ The narrow peak distribution indicates a homogeneous particle population with consistent surface characteristics. This outcome indicates the significant stability of CuO nanoparticles and demonstrates durable electrostatic repulsion force between the nanoparticles, which decreases aggregation.⁹⁶

BET was utilized to calculate the pore volume, size, and surface area of synthesized CuO nanoparticles. An illustration of a type IV isotherm characterized by the H-3 hysteresis loop, as per IUPAC classification, Fig. 2I and J, revealed a mesoporous structure with a significant surface area for CuO nanoparticles, indicating their good potential for the adsorption process. The nanoparticle, based on BET data, exhibits significant pore uptake and an ample surface area. The N₂ adsorption isotherm of the nanoparticles exhibited a BET surface specific area (SSA) of approximately 5.3 m² g⁻¹, a mean pore size of 27.4 nm, and a pore volume of 0.0364 cm³ g⁻¹.

A separate investigation revealed that the BET surface area of synthesized CuO nanoparticles using the simple precipitation method was 1.7 m² g⁻¹, with sizes ranging from 140 to 180 nm.⁹⁷ Another research conducted by Dorner et al. reported that the BET surface area of sol–gel produced CuO nanoparticles was 16 m² g⁻¹, and the size range was 100–140 nm.⁹⁸

3.3. Adsorption study

3.3.1. Effect of the initial concentration. The influence of CR dye concentration on the adsorption process was investigated using different concentrations of 4, 6, 8, 10, 12, and 14 mg L⁻¹, while maintaining constant variables including contact time (60 min), pH (7.0), temperature (20 °C), and nanoparticle dose (0.01 g to 10 mL of CR dye solution). Fig. 3A illustrates that the CR dye removal capability increased as the initial dye concentration decreased. The higher absorption of the CR dye at lower concentrations might be attributed to the higher active site availability on the adsorbent surface for the adsorption of CR molecules. Conversely, at elevated concentrations, these active sites may become quickly saturated, resulting in a limited capacity to adsorb additional CR molecules.⁹⁹ For further experiments, a concentration of 4 mg L⁻¹ of CR dye was utilized as the best setting for the adsorption process which is essential for maximizing removal efficiency.

Fig. 3 The influence of various parameters of CuO NPs on the absorbance and removal of the CR dye. (A) CR dye conc., (B) pH, (C) point of zero charge (pHpzc) analysis, (D) temperature, (E) catalytic dose, (F) initial time, (G) absorbance of the CR dye at different times, (H) the recyclability of CuO NPs for the removal of the CR dye and (I) the absorbance of the CR dye at different cycles.

3.3.2. Effect of pH. The impact of solution pH is essential in the process of adsorption, since it affects the ionization state of the target molecules and characteristic surface of the adsorbent's functional groups. The protonation degree of the active functional groups with acid–base properties in solution is controlled by the pH. The influence of pH has been investigated, while maintaining constant parameters including CR dye concentration (4 ppm), contact time (60 min), temperature (20 °C), and adsorbent dose (0.01 g to 10 mL of CR dye solution), Various pH levels, including 4.0, 5.0, 6.0, 7.0, 9.0, and 11.0, have been studied. The solution pH was modified using 0.1 M solutions of NaOH and HCl. The outcomes, as illustrated in Fig. 3B, revealed that the highest removal capacity of CuO nanoparticles occurred within pH 7.0 and 9.0. Significantly, by reducing the pH of the solution below 5.0 (by adding HCl acid) the colour of the CR dye changes from red to purple; the CR dye becomes very sensitive. The primary peak of the CR dye seen at λ 488–500 nm attempts to move towards the higher wavelength nearly reaching 537 nm at pH 4.0 and 595 nm at pH 2.0 respectively. The bathochromic shift and reduction in the intensity of the CR monomer are ascribed to the partial self-association of CR monomers.¹⁰⁰ Notably, the removal capacity showed low efficiency in acidic medium at pH 5 and 6, but it reached the maximum in neutral medium at pH 7.0 and moderate base at pH 9.0 and started to decrease under strong base condition at pH 12.0. In accordance with these findings, pH 7.0 was chosen as the best condition for the process of adsorption, since the majority of water bodies exhibit a pH range of 6.5 to 7.5; moreover, it provides a neutral environment and reduces possible unpredictability in the mechanism.¹⁰¹ The neutral pH was chosen in the subsequent experiment to enhance adsorption efficacy and to obtain deeper insight into the underlying adsorption mechanism.

The pHpzc of the synthesized CuO nanoparticles was determined using the pH drift method. As shown in Fig. 3C, based on the ΔpH vs. initial pH plot, the pHpzc was identified at approximately pH 9.4. This implies that the CuO surface is positively charged below pH 9.4, which enhances electrostatic attraction with the anionic CR dye, explaining the observed maximum adsorption at pH 7.^72,73 While zeta potential after adsorption was not measured, it is likely that surface interactions with dye molecules shift the surface charge slightly, as commonly reported in similar systems.¹⁰² Although the measured zeta potential was negative (–25 mV), this reflects the effective charge in suspension influenced by phytochemical residues and the ionic environment, whereas the intrinsic oxide surface charge (pHpzc ≈ 9.4) governs the electrostatic attraction with Congo red at pH 7. Therefore, there is no contradiction between the zeta potential value and the observed adsorption behavior.

It should be noted that under strongly acidic conditions, CuO nanoparticles may release small amounts of Cu²⁺, which could affect adsorption behavior; therefore, maintaining near neutral pH is preferable for practical applications.¹⁰³

3.3.3. Effect of temperature. The impact of temperature on the adsorption process of the CR dye has been examined at various temperatures (20, 25, 30, 35, and 40 °C). The other parameters were kept constant, including the CR dye concentration (4 ppm), pH (7.0), time (60 min), and nanoparticle dose (0.01 g in 10 mL of CR dye solution). As shown in Fig. 3D, the removal capacity reached a maximum at a temperature of 20 °C, which is likely due to stronger physical adsorption and favorable adsorption interaction. However, the removal capacity then started to decrease gradually as the temperature increased and reached a minimum at 40 °C; this is possibly due to weakened adsorption forces, desorption of CR molecules and increased thermal agitation. This suggests that the process of adsorption is exothermic.¹⁰⁴ Thus, 20 °C is the optimal temperature, which is important for maximizing removal capacity; this temperature is selected as the best setting for the adsorption process.

3.3.4. Effect of nanoparticle dose. The impact of catalytic dose of CuO nanoparticles on the CR dye has been examined using different dose amounts including 0.01, 0.015, 0.02, 0.025, and 0.03 g per 10 mL of CR dye, fixing other parameters including the initial concentration of CR dye (4 ppm), pH (7.0), temperature (20 °C), and contact duration (60 min). The data as shown in Fig. 3E revealed that the removal percentage of the CR dye rises from 75% to 85% at 0.01 and 0.02 g respectively. This is attributable to the enhanced accessibility of the adsorbent sites at higher doses of the adsorbent. Beyond this optimal dose, the removal capacity begins to decline slightly; the best explanation is agglomeration possibility, which reduces the effective surface area and limits CR dye accessibility to active spots. Thus 0.02 g of the catalyst is the optimal catalytic dose, which is essential for maximizing removal capacity; this dose is selected as the best setting for the adsorption process.

3.3.5. Effect of the initial time. To find out the maximum absorbance capacity of the CR dye at a given time, the influence of time is examined within 60 min., while fixing all other parameters including the concentration of CR dye (4 ppm), pH (7.0), temperature (20 °C), and dose concentration of catalyst (0.02 g per 10 mL of CR dye solution). The absorbance capacity reaches its maximum after only 30 min, as seen in Fig. 3F; the removal capacity increases rapidly within the first 10–15 min, reaching ∼80%, this indicates a fast adsorption rate initially owing to abundant active sites on the nanoparticle surface. The adsorption starts to slow down and the removal efficiency plateaus, suggesting that equilibrium is achieved by around 20–30 min. Beyond this point, no significant improvement in removal efficiency is observed, as the adsorption sites are mostly saturated. Consequently, as shown in Fig. 3G a contact period of 30 min was determined to be optimal for achieving optimum removal efficiency.

3.3.6. Regeneration results. The regeneration of the CuO nanoparticles has been investigated during five cycles via reusing the CuO nanoparticles for fresh solution of the CR dye. As illustrated in Fig. 3H and I, there is no doubt that the adsorption capacity of the adsorbent is maximum during its first cycle. A probable desorption mechanism involves disruption of hydrogen bonding and weakening of electrostatic interactions by ethanol, which lowers the binding affinity of CR dye molecules on the CuO surface.¹⁰⁵ After the use of the adsorbent, a considerable fraction of sites engage in adsorption and are occupied by the molecules of the adsorbate, resulting in the adsorption capacity of the adsorbent being reduced. The regeneration process is an effective technique for the repeated use of the adsorbent. A regenerable adsorbent is both efficient and economical.

3.3.7. Adsorption isotherm. The adsorption data analysis is essential for assessing the adsorption capability of the nanoparticles. Reaching equilibrium, the adsorbate concentration in the solution maintains a dynamic balance with that at the nanoparticle interface, leading to a stable solution concentration.¹⁰⁶

The experimental results for CR dye adsorption on CuO nanoparticles were accurately modeled using the Langmuir equation, with the regression coefficient (R²) equal to 0.9943, Fig. 4A. The Langmuir constants K_L and R_L were determined to be 0.6415 L mg⁻¹ and 0.2804, respectively. R_L indicates that the adsorption process is favorable. The q_max was determined to be 6.9903 mg g⁻¹. Moreover, the Freundlich model demonstrated a linear fit with a coefficient of determination (R²) of 0.9925, Fig. 4B, which is higher than that of the Langmuir model. The Freundlich correlation gives values of 1/n as 0.325 and K_f as 3.0688 mg g⁻¹. A 1/n value below 0.5 indicates favorable adsorption and significant surface heterogeneity, suggesting that multiple classes of active sites are involved in CR uptake.¹⁰⁷

Fig. 4 (A) The line plot of the Langmuir isotherm (solid lines are linear fittings). (B) The line plot of the Freundlich isotherm (solid lines are linear fittings). (C) Pseudo-first-order kinetic model (solid lines are linear fittings). (D) Pseudo-second-order kinetic model (solid lines are linear fittings). (E) Intra-particle diffusion model. (F) Thermodynamic parameters for adsorption of the CR dye onto CuO nanoparticles with time dependency at different temperatures (20, 25, 30, 35, 40, and 45 °C).

To contrast the fitted isotherm models, it is evident from Table 3 that both Langmuir and Freundlich isotherm models provide the optimal fit determined by the coefficient of determination of R².

Table 3 Isotherm results for CR dye adsorption onto CuO NPs (Langmuir and Freundlich). Kinetic results for CR dye adsorption onto CuO NPs: pseudo-first order, pseudo-second order and intra-particle diffusion models. Thermodynamic parameters including standard enthalpy ΔH°, entropy ΔS°, and free energy changes ΔG° for CR dye adsorption

Model	Parameter	Value	Model	Parameter	Value
Langmuir isotherm	q max (mg g^−1)	6.9903	Freundlich isotherm	q max (mg g^−1)	3.0688
	K L (L mg^−1)	0.6415		K f	3.0688
	R L	0.2804		1/n	0.3250
	R ^2	0.9943		R ^2	0.9925
1st order kinetics	q e (mg g^−1)	3.02	2nd order kinetics	q e (mg g^−1)	3.51
	K 1 (min^−1)	0.00252		K 2 (g mg^−1 min^−1)	0.3278
	R ^2	0.4313		R ^2	0.9997
Intra-particle diffusion	k id (mg g^−1 min^−1/2)	0.0906		Intercept C	2.858
	R ^2	0.5823
Thermodynamics	ΔH° (kJ mol^−1)	−34.35	ΔS° (J mol^−1 K^−1)	−106.46
	ΔG° (kJ mol^−1) at 293.15 K	−3.1225	298.15 K	−2.6656
	303.15 K	−2.0634	308.15 K	−1.5105
	313.15 K	−1.0387

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3.3.8. Adsorption kinetics. The adsorption rate of the CR dye on CuO nanoparticles was initially elevated but gradually diminished until equilibrium was reached. After 30 min, the rate of adsorption increased and the highest adsorption was seen; this is defined as the equilibrium time. The kinetics of dye removal are essential factors for assessing adsorption dynamics. Both models, pseudo-first order and pseudo-second order, are revealed in Fig. 4C and D. These models were utilized to correlate the findings and analyze the kinetics of CR dye elimination on CuO nanoparticles.¹⁰⁸

The fit of different kinetic models to the experimental data was investigated using the determination coefficient (R²) and computed adsorption capacities, as presented in Table 3. The higher R² values revealed that the adsorption of the CR dye is more accurately expressed in the pseudo-second-order model. This model's predicted adsorption capacity closely matches the observed experimental values, unlike the pseudo-first-order model. The data indicate that the adsorption process likely entails chemical interactions, including bonding forces or the exchange of ions and electrons, between the CR dye molecules and the CuO nanoparticles.¹⁰⁶

In addition to the pseudo-first order and pseudo-second order models, the adsorption mechanism was further examined using the intra-particle diffusion model (Weber–Morris plot). As shown in Fig. 4E, the plot of q_tversus t^1/2 showed a multilinear relationship with a non-zero intercept. This indicates that while intra-particle diffusion contributes to the overall rate of CR dye adsorption, it is not the sole rate-limiting step. The observed intercept suggests the presence of boundary layer effects during the initial phase, followed by gradual diffusion into the internal pores of the CuO nanoparticles. These findings support a multi-step adsorption process involving both surface adsorption and intra-particle transport, in agreement with prior studies.¹⁰⁹

3.3.9. Thermodynamic studies. The impact of temperature on adsorption is essential for practical applications of the adsorbent, utilized at 20, 25, 30, 35, 40, and 45 °C, Fig. 4F.

The adsorption capacity of CuO nanoparticles for the CR dye was diminished as temperature increased from 20 to 45 °C, suggesting that the adsorption of the CR dye onto the CuO nanoparticles is an exothermic process. The calculated thermodynamic parameter findings are shown in Table 3. Physisorption alters free energy within the range of 20 to 0 kJ mol⁻¹, while chemisorption modifies free energy within the range of 80 to 400 kJ mol⁻¹. The negative results of ΔG° and ΔH° indicate that the process of adsorption is both spontaneous and exothermic. The reaction is verified to be more probable at lower temperature. The negative ΔS° indicates a reduction randomness (orderliness) at the CuO nanoparticles.^104,110

3.3.10. Mechanism of adsorption. The mechanism of adsorption of the CR dye onto CuO nanoparticles utilizing M. sylvestris leaf extract follows both monolayer and multilayer adsorption models, as confirmed by both isotherm models, Langmuir and Freundlich. This suggests that CuO nanoparticles exhibit a variety of adsorption sites with different affinities for CR dye molecules. The adsorption also adheres to pseudo-second order kinetics, indicating a chemisorption-dominated process, wherein the adsorption rate is dependent upon the available active sites of CuO nanoparticles rather than only on CR dye concentration. This indicates strong interactions, including complexation, hydrogen bonding, and electrostatic attraction, between CuO nanoparticles and CR dye molecules. Fig. 5 presents FE-SEM images showing the surface morphology of CuO nanoparticles before and after the adsorption process. Fig. 5A, captured prior to adsorption, displays well-dispersed, highly porous, and uniformly distributed CuO nanoparticles, providing an extensive surface area for the process of CR dye adsorption. Conversely, as shown in Fig. 5B (after adsorption) demonstrates a notable change in surface texture, indicating that the nanoparticles are coated and aggregated as a result of CR dye molecule adsorption. This morphological modification indicates effective adsorption, with CR dye molecules occupying the active sites on the surface of CuO nanoparticles, resulting in a denser and rougher structure.

Fig. 5 FE-SEM images of CuO NPs (A) before adsorption and (B) after adsorption.

To elucidate the adsorption mechanism, a schematic diagram as shown in Fig. 6 illustrates how CR dye molecules interact with CuO nanoparticles through electrostatic attraction between anionic sulfonate groups of CR and positively charged CuO surfaces at pH < pHpzc, hydrogen bonding between CR functional groups and CuO surface hydroxyls, and π–π stacking among aromatic rings. It is further possible that π–π stacking occurs not only directly with phytochemical residues on the CuO surface but also among adsorbed CR molecules, contributing to multilayer adsorption as suggested by the Freundlich model. These synergistic non-covalent interactions align with the observed pH-dependent adsorption behavior, confirming that electrostatic forces predominantly govern the initial adsorption process.^111–113

Fig. 6 Schematic illustration of the adsorption mechanism of the CR dye onto CuO nanoparticles.

3.3.11. Comparative evaluation of adsorbent performance. To better understand the significance of our results, we compared the performance of our nanoparticles with several other Cu-based adsorbents reported in the literature, as shown in Table 4. While some materials such as CuO/Fe₂O₃ bimetallic nanoparticles exhibited higher removal efficiencies (up to 97%) within very short contact times, these systems often involve more complex synthesis routes or costly components. In contrast, our adsorbent achieved a notable 86% removal efficiency in just 30 minutes, without the use of dopants, polymers, or ultrasonic treatment, compared to other green-synthesized CuO-based materials that required up to 120 minutes to reach similar performance; our nanoparticle stands out for its fast kinetics, which is critical for real-time or large-scale water purification applications.

Table 4 Comparison between the published work on adsorption of the CR dye and our work

Nanoparticles	Synthesis	Dye	Adsorbent dose (g L^−1)	Efficiency	Time (min)	Ref.
CuO	Green synthesis	CR and malachite green	4.0	92.3	80	114
CuO/Fe2O3 bimetallic	Green synthesis	CR dye	2.0	97.1	10	115
Mg doped CuO	Green synthesis	CR dye	5.0	81	120	116
ZnO/CuO nanocomposite	Green synthesis	CR dye	2.0	90.1	120	104
CuO	Green synthesis	CR dye	2.0	86	30	Present work

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Importantly, the synthesis route based on the Malva sylvestris extract offers both environmental and economic advantages, avoiding toxic reagents or high-energy inputs. Taken together, these factors position our CuO nanoparticles as a balanced and practical alternative, combining green synthesis, solid efficiency, and operational simplicity for sustainable dye removal.

4. Conclusions

In conclusion, this study successfully demonstrates the green synthesis of CuO nanoparticles using M. sylvestris extract, establishing a cost-effective, eco-friendly, and sustainable approach for nanoparticle production. The resulting CuO nanoparticles exhibited high adsorption efficiency for Congo red dye, with adsorption data fitting well to both Langmuir and Freundlich isotherm models (R² > 0.99), signifying the coexistence of monolayer and multilayer adsorption. Kinetic modelling revealed pseudo-second-order behaviour (R² = 0.9997), signifying that chemisorption governs the rate-limiting step. Thermodynamic analysis confirmed the adsorption process to be spontaneous and exothermic (ΔH° = −34.35 kJ mol⁻¹), involving both physical and chemical interactions, as evidenced by the negative entropy change (ΔS° = −106.46 J mol⁻¹ K⁻¹). Importantly, the CuO nanoparticles retained over 78% adsorption capacity after four regeneration cycles, underscoring their practical reusability. Collectively, these findings highlight the potential of phyto-synthesized CuO nanoparticles as an efficient, renewable, and economically viable adsorbent for the removal of synthetic dyes from wastewater, supporting their applications in sustainable environmental remediation technologies.

Consent for publication

The authors agreed to publish the manuscript.

Ethics approval and consent to participate

Ethical approval was not required for this study as it did not involve human participants, animals, or sensitive data.

Author contributions

AJM: conceptualization, data curation, formal analysis, investigation, methodology, resources, software, visualisation, writing original draft; KMO: supervision, methodology, writing – review and editing; AKQ: supervision, validation, writing – review and editing.

Conflicts of interest

No potential conflict of interest was reported by the author(s).

Data availability

The datasets used and analysed during the current study are available from the corresponding author upon request.

Acknowledgements

This research received no specific grant from funding agencies. The authors would like to thank the Ministry of Higher Education and Scientific Research in the Kurdistan Region and the University of Zakho and Akre University for applied science for their support.

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