Introduction

Industrialization has spurred significant economic growth but has also led to unprecedented environmental challenges, particularly concerning water pollution1,2. The discharge of untreated or inadequately treated industrial wastewater into water bodies has resulted in the contamination of aquatic ecosystems and posed risks to public health and environmental sustainability3,4. Among the pollutants commonly found in industrial wastewater, chromium, particularly in its hexavalent form (Cr (VI)), is of significant concern due to its toxicity and detrimental effects on human health and the environment5. Chromium contamination stems from various industrial activities such as textiles, electroplating, steel production, and tanneries6. Hexavalent chromium is highly soluble and mobile, making it particularly challenging to remove from wastewater7. The detrimental effects of chromium on human health include bronchial asthma, lung cancer, nasal ulcers, skin allergies, carcinogenicity, and genotoxicity. Moreover, chromium contamination in water and soil can disrupt the ecological balance of aquatic environments and pose risks to aquatic organisms8.

To address these concerns, the World Health Organization (WHO) has set a guideline value of 50 µg/L for total chromium in drinking water, whereas the European Union (EU) has established a higher limit of 100 µg/L under its Drinking Water Directive. However, the WHO has not provided a specific guideline for hexavalent chromium9,10. While regulations for wastewater may vary, these drinking water standards serve as important benchmarks for assessing acceptable chromium levels in treated water, helping to ensure safety and mitigate environmental impact.

Conventional wastewater treatment methods often face limitations in effectively decontaminating chromium from wastewater7. These methods, such as ion exchange, coagulation, and chemical precipitation, can be labor-intensive, and costly. It also releases toxic components into the environment11,12. Therefore, there is a pressing need for the development of advanced wastewater treatment technologies to mitigate chromium contamination. Adsorption has emerged as a promising technique for chromium removal from wastewater due to its cost-effectiveness, environmental friendliness, and ease of design13,14. Various types of adsorbents have been investigated for their effectiveness in detoxifying chromium-containing wastewater. Among these, biomass-derived activated carbons (BACs) have shown promise as they offer a sustainable and cost-effective solution for pollutant removal15,16.

Rumex Abyssinicus, a plant species with diverse properties, has attracted attention for its potential applications in wastewater treatment. Activated carbon derived from Rumex Abyssinicus biomass has shown promising adsorption properties, particularly in the removal of various contaminants from water and wastewater17. However, the comprehensive cost analysis has revealed challenges associated with chemical activation, prompting the exploration of Rumex Abyssinicus stem-based biosorbent as a potential alternative18. Research has been conducted on using Rumex Abyssinicus-derived biosorbent for the removal of different pollutants such as methyl orange19, Hexavalent chromium20, methylene blue21, malachite green dye22, and Rhodamine B23.

Red clay has been explored as an effective adsorbent for the removal of Cr (VI) from wastewater; however, research in this area remains limited. Studies have reported a maximum adsorption capacity of 1.3 mg/g when utilizing red clay as an adsorbent24. Additionally, investigations into modified red clay, particularly with hexadecyltrimethylammonium bromide (HDTMA), have demonstrated a maximum Cr (VI) removal efficiency of 90%25.

Magnetite, with its superparamagnetic properties, is non-toxic and readily available, enhancing the structure, active sites, and functionalities of composites. This augmentation leads to improved adsorption performance, structural stability, increased surface area, and greater selectivity towards target pollutants26,27.

Graphene oxide, as a component for nanocomposites, contributes high surface area, mechanical strength, and chemical stability. Its dispersibility in aqueous solutions allows for uniform distribution within the adsorbent matrix, offering abundant active sites for pollutant interaction and enhancing adsorption efficiency28. Moreover, graphene oxide's versatility and ease of functionalization enable tailoring properties for specific applications. The inclusion of these components in composites addresses challenges associated with sludge separation, and streamlining wastewater treatment. Research demonstrates that nanocomposites significantly reduce reaction times and enhance adsorption efficiency, emphasizing the potential of composite adsorbents in advancing sustainable water treatment practices29.

Composite adsorbents are gaining attention as viable alternatives to address the shortcomings of biomass and clay-based adsorbents in wastewater treatment and pollutant removal30. By combining various components, these materials aim to harness the strengths of each to form a more efficient adsorption system. Magnetite and graphene oxide are frequently incorporated into composite adsorbents, providing distinct benefits31,32. These composite was advantageous in removing Cr(VI) from water, balancing cost-effectiveness, reliability, and high performance33. The use of inexpensive and abundant materials like activated carbon and bentonite clay helps keep costs low, while the composite's high adsorption capacity reduces the amount needed and lowers operational costs through energy-efficient synthesis34. Enhanced stability and durability come from graphene oxide's mechanical strength and Fe3O4 magnetic properties, ensuring consistent performance and resistance to fouling. The combination of these materials results in a high surface area and numerous active sites, leading to superior adsorption and removal efficiency35. Magnetic separation enabled by Fe3O4 facilitates easy recovery and reuse of the adsorbent, enhancing cost-effectiveness and reducing secondary pollution36. The composites disadvantage might be Synthesis Complexity, the fabrication of this nanocomposite may involve multiple steps and precise control over conditions, which can be technically challenging and time-consuming37.

Many researchers presented the Comprehensive use of the Response Surface Methodology (RSM) as an effective, and scalable statistical modeling method in approaching chromium removal, and investigated the practical applicability of the nanocomposite material38,39,40. In addition, Various researchers have investigated the adsorption of Cr (VI) using different adsorbents such as15,16,41. However, it is noted that no research has been conducted on the Rumex Abyssinicus-derived Activated Carbon/Bentonite Clay/Graphene Oxide/Iron Oxide (RAAC/AABC/GO/Fe3O4) nanocomposite for the removal of Chromium. Therefore, this study aims to investigate the potential of the RAAC/AABC/GO/Fe3O4 nanocomposite for chromium for removing chromium from an aqueous solution.

Materials and methods

Materials, chemicals, and equipment

To synthesize the nanocomposite material, various analytical quality chemicals and reagents were applied: Phosphoric acid (85%, H3PO4), Sulfuric acid (97%, H2SO4), Sodium nitrate (99%, NaNO3), Potassium permanganate (99.9%, KMnO4), Hydrogen peroxide (H2O2), graphite (C), hydrated iron sulfate (FeSO4.7H2O), sodium hydroxide (97%, NaOH), Potassium dichromate (K2Cr2O7), and 1,5-Diphenylcarbazide (DPC). The filter paper (Whatman, 150 mm Ø) was used for the solid–liquid separation process and most chemicals were manufactured by Merck Company (Munich, Germany). In addition, the whole solutions were prepared with distilled water.

A copper oven and a calibrated pH meter (Model HANNA 83141) were utilized for drying processes and for measuring acidity and basicity, respectively. The surface area of the samples was analyzed using the Brunauer–Emmett–Teller (HORIBA Scientific SA-9600, Japan). The morphological structures were examined with a scanning electron microscope (Model FEI Inspect F50, Germany). A UV spectrophotometer (JASCO V-770, Italy) was used to measure the absorbance of a solution to determine its concentration. XRD (Olympus BTXH, Japan) was utilized to determine the crystalline structure and phase composition of the samples. FTIR (Shimadzu IR Affinity, Japan) was applied to analyze the molecular and chemical structure by measuring infrared absorption spectra.

Adsorbent preparation

Preparation of Rumex Abysinicus driven activated carbon (RAAC)

The preparation of powdered biosorbent material derived from Rumex Abyssinicus stems commenced with the collection of specimens from the premises of Addis Ababa Science and Technology University (AASTU), Addis Ababa, Ethiopia, cross-verified against the herbarium of Addis Ababa University to ensure accuracy. Permission to collect Rumex Abyssinicus in Ethiopia was granted by the National Herbarium of Addis Ababa University (ETH), with support from a letter provided by AASTU. The plant material was formally identified by Dr. Weldemariam Seifu, a botanist at Addis Ababa University. The voucher specimens, although not yet assigned a Voucher ID, have been deposited at the National Herbarium of Addis Ababa University (ETH). The use of plant parts in this study adheres to international, national, and institutional guidelines. Following meticulous washing with distilled water to eliminate impurities, the material underwent overnight oven drying at 105 °C to attain optimal crispiness. Subsequent pulverization and sieving yielded particle sizes of 150 μm, culminating in storage within an airtight container. Then the oven-dried sample is impregnated with an 88% phosphoric acid solution at a 1:1 ratio, followed by pyrolysis at 500 °C for 2 h. The resulting carbonized samples were washed with distilled water to remove residual phosphoric acid, dried, and stored in a zip-lock plastic bag for subsequent characterization and batch adsorption optimization tests, ensuring adherence to regulatory standards throughout the process18,20.

Preparation of acid activated bentonite clay (AABC)

The specimens were obtained from the premises of AASTU and underwent a thorough cleansing process to eliminate any adhered impurities, accomplished by washing them meticulously with distilled water. Following this, the specimens were subjected to drying in an oven at 105 °C and subsequently ground into 150 µm particle size using a high-speed multi-function miller. Chemical activation was then carried out by immersing 10 g of red clay in 100 mL of 2 M H2SO4 solution, maintaining a ratio of 1:10, and allowing it to soak for 2 h at 80 °C. The resulting acid-activated bentonite clay (AABC) underwent multiple washes with distilled water until reaching a neutral pH level42.

Preparation of graphene oxide (GO)

Graphene oxide (GO) has recently gained prominence as an effective adsorbent in water and wastewater treatment applications. The synthesis method for GO can vary depending on the specific experiment or study. In this particular synthesis approach, graphite was utilized to produce black GO powder. The process involved dissolving 2 g of graphite and 1 g of NaNO3 in 46 mL of 98% H2SO4, followed by stirring with a magnetic stirrer at room temperature for 1 h. Subsequently, 6 g of KMnO4 was gradually introduced to the solution while stirring continuously. The temperature of the solution was then raised to 40 °C and maintained for 3 h before cooling back to room temperature. At a temperature of 50 °C, 250 mL of water was slowly added to the solution and stirred for an additional hr until it turned brown. During this stirring, 10 mL of 30% H2O2 was incrementally added to the solution. The resulting mixture underwent sonication for 30 min, leading to the synthesis of graphene oxide. Following synthesis, the solution was filtered, and the filtrate underwent continuous washing until the pH reached a neutral state. Finally, the graphite oxide powder was heated at 100 °C for 5 h, rendering it suitable for hybridization with magnetite for wastewater treatment purposes43,44.

Preparation of iron oxide (Fe3O4)

The synthesis of Fe3O4 followed the co-precipitation method. Initially, 2 g of NaNO3 and 3.3 g of FeSO4.7H2O were dissolved in 50 mL of distilled water and stirred for 60 min. Subsequently, the pH of the solution was adjusted to 12 using 1 M NaOH solution, followed by continuous stirring at 80 °C for 5 h to facilitate precipitation. After cooling to room temperature (25 ± 1 °C), the precipitate was separated using a permanent magnet, and any residual chemicals were removed through multiple washes with distilled water until reaching a neutral pH. The resulting precipitate was then oven-dried at 60 °C for 24 h and stored in an airtight plastic bag until needed for the production of the nanocomposite43,45.

Preparation of RAAC/AABC/GO/Fe3O4 nanocomposite

The chemical co-precipitation method was utilized to bind nanoparticles (GO and Fe3O4) with the RAAC/AABC composite46. Initially, RAAC and AABC were mixed at a 1:1 ratio. Simultaneously, the synthesis of GO/Fe3O4 nanocomposites was conducted using an in situ coprecipitation technique. Initially, 1 g of GO was dispersed in 50 mL of distilled water and sonicated for 10 min to ensure homogeneous dispersion. Following this, 5 g of Fe3O4 were introduced into the solution, followed by an additional 30 min sonication period. Subsequently, the solution underwent heating at 100 °C for 5 h. Finally, the resultant slurry was subjected to calcination in a fuming furnace at 700 °C for 2 h to yield the desired nanocomposites43.

Following this, the RAAC/AABC composite was combined with GO/Fe3O4 at a ratio of 2:1 in a beaker, followed by the addition of 100 mL of distilled water to the mixture. A 5 M NaOH solution was gradually introduced into the mixture until reaching a pH value of 11 to induce chemical precipitation of the nanoparticles on the surface of the RAAC/AABC composite. The mixture underwent heating and continuous stirring for 1 h at 65 °C. Afterward, the RAAC/AABC/GO/Fe3O4 nanocomposite was allowed to cool to room temperature and subjected to multiple washes with distilled water until reaching a neutral pH. Subsequently, the nanocomposite was dried in an oven at 80 °C for 24 h and stored in an airtight plastic bag until required for treatment47,48. The various stages of preparing the nanocomposite are illustrated in Fig. 1.

Figure 1
figure 1

The different steps involved in the preparation of the RAAC/AABC/GO/Fe3O4 nanocomposite (A-Rumex Abysinicus, B- RAAC, C-AABC, D-RAAC/AABC Composite, E-GO, F-Fe3O4, G-GO-Fe3O4 nanocomposite, H-RAAC/AABC/GO/Fe3O4 nanocomposite).

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Adsorbent characterization

Proximate analysis

The moisture content (MC), volatile matter (VM), ash content (AC), and fixed carbon (FC) of the nanocomposite were evaluated following ASTM standards (D 2866–2869)49.

Scanning electron microscope (SEM)

The morphological attributes of the nanocomposite were examined using scanning electron microscopy (SEM) at 5.00 kV. Various resolutions were employed to analyze the morphology of the nanocomposite both pre and post-adsorption. Throughout the sample preparation and scanning procedures, standard operating protocols for SEM were followed rigorously, utilizing a Model FEI Inspect F50 instrument from the United States, as outlined in previous studies50.

X-ray diffraction (XRD)

X-ray diffraction (XRD) analysis was conducted using an Olympus BTXH model, covering a diffraction angle range from 10° to 80°, to assess the crystalline and amorphous properties of the sample pre-adsorption and post-adsorption. The XRD instrument operated at a current of 15 mA with a scanning rate of 4.2 °C/min. Debye Scherrer equation was used to find out the average crystallite size of RAAC/AABC/GO/Fe3O4 nanocomposite before and after adsorption using Eq. (1). Data analysis was performed using Origin Software Version 9.5549.

$$ {\text{D}} = \frac{{{\text{k}}\lambda }}{{{\text{B}} \cos \theta }} $$
(1)

where B is the full-width half maximum (FWHM), k (Scherrer constant \(=\) 0.9), D is the crystallite size (nm), and λ is the wavelength of X-ray (λ \(=\) 0.15406).

Fourier-transform infrared spectroscopy (FTIR)

The functional groups present in the nanocomposite were identified using a Fourier-transform infrared (FTIR) spectrophotometer. The samples were combined with potassium bromide (KBr) at a ratio of 2:200, crushed using a mortar, and applied onto a ZnSe crystal under pressure. FTIR analysis of the functional groups of AC, both before and after adsorption, was conducted using a Shimadzu IR Affinity-1 s spectrophotometer from Japan, covering the spectral range of 4000–400 cm−1. Data analysis was performed using Origin Software (Version 9.55), as detailed in previous studies5.

Brunauer–Emmett–teller (BET)

The examination of the sample's surface area consisted of placing 0.4 g of the nanocomposite sample into three vacuum tubes and subjecting them to a 2 h heating process at 100 °C, as per the procedure outlined by Fito et al. (2020). Subsequently, the surface area of the nanocomposite, both pre- and post-adsorption, was determined using the BET surface area analyzer, specifically the Horiba SA-9600 model.

Determination of pH at point of zero charge (pHPZC)

The salt addition technique was employed to determine the point of zero charge (pHPZC). Five solutions of 50 mL 0.01 M NaCl were prepared in 250 mL Erlenmeyer flasks. The pH of these solutions was adjusted from 3 to 11 using 0.01 M NaOH and 0.01 M H2SO4 solutions. Each solution was then supplemented with 0.5 g of the nanocomposite and mixed at 150 rpm for 36 h using an orbital shaker (Model SK-300) at room temperature (25 °C). Subsequently, the final pH of the solutions was measured using a portable pH meter (Model HANNA 83,141), and the changes in pH (ΔpH) were calculated. A graph depicting the change in pH versus the initial pH was plotted using Microsoft Excel 2016, allowing for the determination of pHPZC at the x-intercept.

Preparation of aqueous solution

The stock solution preparation process began with the dissolution of 2.82 g of K2Cr2O7 in 1 L of distilled water, resulting in a 1000 mg/L Cr stock solution. Subsequently, the working solution of chromium was prepared through a dilution procedure.

Optimization of Cr (VI) removal

The concentration of Cr (VI) was predetermined based on the experimental design outlined in Table 146, drawing from both experimental data and literature sources. The experimental setup employed RSM with a Central Composite Design (CCD) (α = 1) to systematically investigate the effects of four factors, each at three levels. The factors included: pH values of 3, 6, and 9; initial Cr (VI) concentrations of 40, 70, and 100 mg/L; contact times of 60, 90, and 120 min; and adsorbent doses of 0.5, 0.75, and 1 g. Removal efficiency of the adsorbent material was gauged by calculating removal percentages. All trials were conducted in 250 mL Erlenmeyer flasks containing 200 mL of working solution, placed on an orbital shaker (Model SK-300) at room temperature (25 ± 1 °C), with a mixing speed of 300 rpm, employing a batch mode delivery system5. The adsorption process was investigated using RSM with a CCD, which included 30 experimental runs (24 factorial points and 6 center points). The data were analyzed using Design Expert ® software, version 12.0.

Table 1 Adsorption factors along with their corresponding levels as per the full factorial design.
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The total number of experiments (N) was calculated using Eq. 2.

$$N = {2}^{\text{F}}+ 2F + x0$$
(2)

where F represents the number of factors and xo indicates the number of replicates at the central point.

In this study, N equaled 30, F equaled 4, and xo equaled 6. The experimental order followed a random selection method. Following each adsorption trial, the solution underwent filtration using Whatman filter paper, and the concentration of Cr (VI) ions was determined using a UV-spectrophotometer (JASCO V-770) at a wavelength of 540 nm, utilizing diphenylcarbazide (DPC) as the complexing agent.

Removal efficiency (Re) and adsorption capacity (qe) were computed using Eqs. (3, and 4) respectively.

$$Re=\left(\frac{Co-Ce}{Co}\right)*100$$
(3)
$$qe=\left(\frac{Co-Ce}{m}\right)*V$$
(4)

where Re represents the removal efficiency (%), qe denotes the adsorption capacity (mg/g), Co stands for the initial Cr (VI) concentration (mg/L), Ce indicates the final Cr (VI) concentration (mg/L), V represents the volume of the adsorbate (L), and m signifies the mass of adsorbent (g).

The regression analysis was carried out using the quadratic regression model established through RSM within CCD. The quadratic model is expressed by Eq. (5).

$$ R = \beta {\text{o}} + \sum\limits_{i = 1}^{k} {\beta {\text{ixi}}} \sum\limits_{i = 1}^{k} {\beta {\text{ii}}\left( {{\text{xi}}} \right)^{2} } + \sum\limits_{i = 1}^{k} \cdot \sum\limits_{i = 1}^{k} {\beta {\text{ijxixj}}} $$
(5)

where R stands for the forecasted outcome, with β0 representing the constant coefficient, βi indicating the coefficients for linear effects, βii denoting coefficients for quadratic effects, and βij signifying coefficients for interaction effects. xi and xj represent the independent variables, while K signifies the total number of independent variables involved in the equation.

Adsorption isotherm

The adsorption isotherm was investigated under optimal conditions of pH (6), contact time (90 min), adsorbent dose (1 g/200 mL), and varying initial concentrations (40, 50, 70, 80, and 100 mg/L). The experiments were conducted at room temperature in 250 mL Erlenmeyer flasks containing 200 mL of working solution, with a mixing speed of 300 rpm. Langmuir, Freundlich, and Temkin isotherm models were employed to examine the interaction between the adsorbent and the adsorbate at equilibrium. The Langmuir isotherm postulates the presence of homogeneous active sites on the adsorbent surface52. Its nonlinear mathematical expression is represented by Eq. (6).

$$qe=\left(\frac{qm*KL*Ce}{1+\left(KL*Ce\right)}\right)$$
(6)

where qm represents the maximum adsorption capacity of Cr (VI) per unit mass of activated carbon (mg/g), KL denotes the Langmuir constant (L/mg), qe denotes the adsorption capacity (mg/g), and Ce represents the equilibrium Cr (VI) concentration (mg/L).

The separation factor (RL) is calculated using Eq. (7).

$$\text{RL}= \frac{1}{1+KLCo}$$
(7)

The adsorbent is deemed unsuitable for adsorption if RL > 1. If RL = 0, adsorption is reversible. A linear adsorption type is indicated when RL = 1 and the adsorbent is deemed suitable if 0 < RL < 1.

Freundlich adsorption isotherm suggests that the adsorbate is adsorbed onto a heterogeneous adsorbent53. The nonlinear model equation is represented by Eq. (8).

$$qeq=(Kfr*Ce{q}^\frac{1}{nfr})$$
(8)

Where Kfr denotes the Freundlich constant (L/mg), and nfr denotes the adsorption intensity.

The Freundlich constant Kfr evaluates the power of adsorption or the relative capacity of the adsorbent.

The Temkin adsorption isotherm assumes that as the coverage of the adsorbent surface increases, the heat of adsorption for all molecules decreases54. The nonlinear mathematical expression of this model is represented by Eq. (9).

$$qe=B*Ln\left(A*Ce\right)$$
(9)

where A denotes the equilibrium binding constant, which is related to the maximum binding energy (L/g), and B denotes the Temkin constants, which are related to the heat of sorption (J/mol).

Adsorption kinetics and intraparticle diffusion

The adsorption kinetics reflect the rate at which the adsorbate resides at the solid-solution interface55. The removal rate of solutes is controlled by the proportion of adsorbed molecules and the rate at which molecules arrive at the adsorbent surface. The adsorption kinetics were investigated under optimal conditions of pH (6), adsorbent dose (1 g/200 mL), initial Cr (VI) concentration (70 mg/L), and varying contact times (60, 70, 80, 100, and 120 min). The experiments were conducted in a 200 mL working solution within a 250 mL Erlenmeyer flask at room temperature, with a mixing speed of 300 rpm. The solid adsorption capacity is described by the nonlinear forms of pseudo-first-order and pseudo-second-order kinetics models, as depicted in Eqs. (10 and 11), respectively.

$$qt=qe*({1-e}^{-k1*t})$$
(10)
$$qt=\left(\frac{q{e}^{2}*K2*t}{1+\left(qe*k2*t\right)}\right)$$
(11)

where qt represents the amount of Cr (VI) adsorbed at a time (t) (mg/g), K1 and K2 denote the pseudo-first-order and pseudo-second-order rate constants, respectively (g/mg min), m is the mass of adsorbent (g), V is the volume of the solution (L), and t is the contact time (min).

The Weber-Morris intraparticle diffusion model is employed to investigate the diffusion mechanism of Cr (VI). This model helps determine if intraparticle diffusion is the sole rate-controlling process56. The model equation is represented by Eq. (12).

$$qt=Kid*{t}^{0.5}+C$$
(12)

where kid denotes the intraparticle diffusion rate constant (mg/g min0.5), and c denotes a constant number.

Result and discussion

Adsorbent characteristics

Proximate analysis is crucial for determining the composition of a nanocomposite adsorbent, essential for its characterization and assessing its potential use. In this study, the nanocomposite adsorbent displayed a moisture content of 3.94%, indicating internal water presence. The volatile matter accounted for 11.85% of the adsorbent's mass, suggesting the presence of components that vaporize easily, which could affect its adsorption properties. The ash content, at 3.84%, represents the residual inorganic material left after combustion, providing information about the purity and mineral content of the adsorbent. Most notably, fixed carbon made up 80.37% of the adsorbent, emphasizing the abundance of stable carbonaceous material crucial for adsorption processes, as shown in Table 2. The low moisture content and high fixed carbon percentage indicate that the nanocomposite has desirable qualities for a high-performance adsorbent57. This detailed proximate analysis reveals the complex composition of the nanocomposite adsorbent, offering valuable insights into its potential applications in various industrial and environmental fields.

Table 2 The proximate composition of the nanocomposite in terms of the weight percentages.
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The SEM analysis illustrated the surface morphology of the nanocomposite before and after chromium adsorption. Before adsorption, the SEM image (Fig. 2A) showcased porous and heterogeneous surfaces, facilitating pollutant adsorption due to increased active site concentration resulting from chemical activation and thermal treatment. Additionally, the uneven distribution of surface ups and downs, with dark areas indicating activated surface pores, suggested the potential for multi-pollutant interaction58. However, post-adsorption (Fig. 2B), the surface exhibited a smoother appearance, signifying chromium adsorption5.

Figure 2
figure 2

SEM image Before Adsorption (A), and After Adsorption (B).

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The XRD analysis of the nanocomposite adsorbent before and after adsorption revealed significant structural alterations as shown in Fig. 3. Before adsorption, characteristic peaks were observed at corrected 2θ values of 14°, 18°, 21°, 24°, 25°, 27°, 31°, 36°, 42°, 50°, 54°, and 61°, indicative of its original composition, likely corresponding to minerals such as quartz (2θ = 25°), calcite (2θ = 29°), and kaolinite (2θ = 36°). However, after adsorption, shifts in peaks were evident, with distinct peaks appearing at corrected 2θ values of 11°, 20°, 21°, 23°, 25°, 29°, 36°, 44°, 49°, 56°, 61°, and 63°. These changes signify structural modifications due to adsorption, suggesting the interaction and incorporation of adsorbate species into the nanocomposite matrix. Notably, the appearance of new peaks at 2θ = 11° and 44° may indicate the presence of new mineral phases formed during the adsorption process, potentially corresponding to chromium-containing compounds. The presence of high-intensity peaks signifies the existence of a crystalline structure, while a decrease in peak intensity with increasing 2θ values in the spectra pattern suggests a predominant presence of an amorphous carbon arrangement50. The average crystallite sizes of before adsorption and after adsorption samples were 8.5 and 11 nm respectively, indicating that the increase in average crystallite size suggests that the adsorption process has caused structural changes in the material. This could be due to mechanisms such as crystallite growth, surface coverage, or energy-driven structural rearrangement59,60,61. This XRD analysis provides valuable insights into the structural transformations occurring during the adsorption process, elucidating the mechanism underlying the adsorption phenomenon in the nanocomposite adsorbent.

Figure 3
figure 3

XRD analysis before and after analysis.

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The FTIR analysis was conducted to elucidate the mechanism of chromium adsorption by the nanocomposite adsorbent composed of Rumex Abyssinicus derived activated carbon, bentonite clay, graphene oxide, and iron oxide as shown in Fig. 4. Before adsorption, prominent peaks were observed at wavenumbers of 463 cm−1, 1012 cm−1, 1635 cm−1, 3372 cm−1, and 3620 cm−1, corresponding to specific functional groups present in the nanocomposite. The peak at 463 cm−1 is indicative of Fe–O stretching vibrations, suggesting the presence of iron oxide. The peak at 1012 cm−1 can be attributed to Si–O stretching vibrations, characteristic of bentonite clay. The peak at 1635 cm−1 corresponds to C=C stretching vibrations in graphene oxide, while peaks at 3372 cm−1 and 3620 cm−1 are indicative of O–H stretching vibrations in hydroxyl groups present in activated carbon and bentonite clay, respectively. After chromium adsorption, shifts and changes in peak intensities were observed. Specifically, peaks were observed at 458 cm−1, 1010 cm−1, 1580 cm−1, 3387 cm−1, and 3623 cm−1. The peak at 458 cm−1 may indicate the formation of Cr–O bonds, suggesting chromium adsorption onto iron oxide. The peak at 1010 cm−1 corresponds to Si–O stretching vibrations, indicating bentonite clay's continued presence. The peak at 1580 cm−1 suggests the retention of C=C stretching vibrations in graphene oxide. Furthermore, peaks at 3387 cm−1 and 3623 cm−1 indicate the presence of O–H stretching vibrations, possibly due to hydroxyl groups involved in chromium adsorption. These FTIR analyses provide valuable insights into the interaction mechanisms between chromium ions and functional groups present in the nanocomposite adsorbent, elucidating its effectiveness in chromium removal from aqueous solutions.

Figure 4
figure 4

FTIR analysis before and after analysis.

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The BET analysis was employed to evaluate the surface area of the nanocomposite, providing crucial insights into its adsorption capacity62. Pre-adsorption, the nanocomposite showcased a substantial surface area of 1201.23 g/m2, attributed to the porous nature of its components. The presence of activated carbon and graphene oxide, known for their inherent porosity, along with bentonite clay and iron oxide, further contributed to this heightened surface area. However, post-adsorption, a notable decline in surface area to 540.63 g/m2 was observed, indicating effective chromium ion binding onto the nanocomposite surface, potentially resulting in pore blockage or surface coverage63. This BET analysis underscores the significant surface area available for adsorption pre-adsorption and the subsequent decrease post-adsorption, indicative of chromium uptake by the nanocomposite. These results affirm the nanocomposite's efficacy in chromium removal and offer insights into its adsorption mechanisms and performance enhancements.

The nanocomposite adsorbent's pHpzc value, determined to be 8.25 (Fig. 5), serves as a critical parameter in understanding its surface charge behavior and adsorption mechanisms. This characteristic value represents the pH at which the nanocomposite surface carries no net charge, indicating its transition from predominantly negatively charged to positively charged as the pH increases. Comprehending the pHpzc of the adsorbent is essential for optimizing its adsorption performance, as it influences the electrostatic interactions between the adsorbent surface and the adsorbate species64,65. At pH values above the pHpzc, the nanocomposite surface carries a negative charge, facilitating the adsorption of positively charged species through electrostatic attraction. Conversely, at pH values below the pHpzc, the surface becomes positively charged, favoring the adsorption of negatively charged species. This nuanced understanding of the nanocomposite's surface charge behavior based on its pHpzc value is crucial for tailoring adsorption processes and enhancing the efficiency of pollutant removal in various environmental remediation applications.

Figure 5
figure 5

Point of zero charge (pHpzc).

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Optimization of Cr (VI) removal

The batch experimental results demonstrated a removal efficiency of hexavalent chromium Cr (VI) ranging from 95.30 to 99.91%, achieved without the presence of interfering ions. The maximum removal efficiency of 99.91% was observed at optimal conditions of pH 2, initial Cr (VI) concentration 70 mg/L, contact time 90 min, and adsorbent dose 1 g/200 mL as shown in Table 3. Conversely, the minimum removal of 95.30% occurred under experimental conditions of pH 9, initial Cr (VI) concentration 40 mg/L, contact time 60 min, and adsorbent dose 0.5 g/200 mL. Noteworthy variations in removal efficiency were observed under varying pH, initial Cr (VI) concentration, contact time, and adsorbent dose, highlighting the significant influence of these parameters on the adsorption process. The nanocomposite adsorbent exhibited exceptional removal efficiency exceeding 99% at pH 6, indicating its effectiveness under acidic conditions, while slightly lower efficiencies were observed at pH 9, suggesting the importance of electrostatic interactions. Additionally, higher concentrations and doses generally led to increased removal, while longer contact times showed diminishing returns. These results emphasize the efficacy of the nanocomposite adsorbent for Cr (VI) removal and offer valuable insights into optimizing adsorption conditions for improved pollutant removal in water treatment applications.

Table 3 Experimental results relating to the Cr (VI).
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Analysis of variance (ANOVA) and regression equation

The Model F-value of 50.06 indicates that the model is statistically significant, with only a 0.01% chance of obtaining such a high F-value purely by random variation. P-values below 0.0500 signify that the model terms B, C, D, AD, BC, CD, A2, and B2 are significant, as detailed in Table 4. Terms with p-values above 0.1000 are considered non-significant, suggesting opportunities for simplifying the model to improve its efficiency, provided key terms for hierarchy support are retained. The Lack of Fit F-value of 1.93 shows that the lack of fit is not significant compared to pure error, with a 24.19% probability of this value being due to random noise, which is a favorable outcome. The Predicted R2 of 0.9176 is closely aligned with the Adjusted R2 of 0.9595, reflecting strong consistency. The Adeq Precision, which measures the ratio of signal to noise, exceeds the acceptable level at 24.888, indicating sufficient signal strength. Consequently, the quadratic model is considered appropriate for predicting COD removal and navigating the design space effectively.

Table 4 ANOVA for the removal of Cr (VI) using a quadratic model within RSM-CCD.
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The equation used for predicting Cr (VI) removal is depicted in Eq. (13).

$$ {\text{Y }} = { 99}.{6}0 \, - \, 0.{\text{455556B }} + \, 0.{\text{761111C }} + \, 0.{\text{561667D }} + \, 0.{\text{16875AD }} - \, 0.{\text{28125BC }} - \, 0.{\text{39375CD }}{-}{ 1}.{\text{28991A}}^{{2}} {-} \, 0.{\text{989912B}}^{{2}} $$
(13)

where Y indicates the predicted Cr (VI) removal, A represents pH, B indicates initial Cr (VI) concentration, C signifies contact time, D indicates adsorbent dose, AD stands for the interaction between pH and adsorbent dose, BC denotes the interaction between initial Cr (VI) concentration and contact time, CD denotes the interaction between contact time and adsorbent dose, A2 reflects the interaction among pH, and B2 represents the interaction among initial Cr (VI) concentration.

Any statistically insignificant values (P > 0.05) were eliminated from the model equation. As depicted in Table 4, terms B, C, D, AD, BC, CD, A2, and B2 are statistically significant (P < 0.05), while A, AB, AC, BD, C2, and D2 are not statistically significant (P > 0.05). These significant factors and interactions play a crucial role in Cr (VI) removal from the aqueous solution.

The interaction effects of factors on Cr (VI) adsorption

The interaction effect of pH and adsorbent dose

In exploring the interaction effect between pH and adsorbent dose, a consistent initial Cr (VI) concentration and contact time were maintained at 70 mg/L and 90 min respectively as shown in Fig. 6. The pH was systematically varied from 3 to 9, while the adsorbent dosage ranged from 0.5 to 1 g. A notable observation emerged as the pH increased from 3 to 6, coinciding with a corresponding enhancement in Cr (VI) removal efficiency from 97.9 to 99.5%. This augmentation could be attributed to the alteration of surface charge and ionization characteristics of the adsorbent, facilitating increased adsorption sites66. However, at pH 9, a slight decline in removal efficiency to 98.4% was noted, possibly due to the formation of hydroxide complexes hindering adsorption kinetics67. Conversely, escalating the adsorbent dosage from 0.5 to 1 g/200 mL resulted in a progressive enhancement in removal efficiency from 98.7 to 99.91%. This enhancement may stem from the increased availability of active sites on the adsorbent surface, enhancing the capacity for Cr (VI) binding68.

Figure 6
figure 6

The interaction effect of pH and adsorbent dose (AD).

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The interaction effect of initial Cr (VI) concentration and contact time

The interaction effect between initial Cr (VI) concentration and contact time was examined while maintaining constant pH (6) and adsorbent dose (0.75 g/200 mL), with variations in initial Cr (VI) concentration from 40 to 100 mg/L and contact time from 60 to 120 min as shown in Fig. 7. Notably, as the initial Cr (VI) concentration ascended from 40 to 70 mg/L, there was a gradual rise in removal efficiency from 99.1 to 99.3%. This phenomenon could be attributed to the increased concentration gradient, favoring diffusion into the adsorbent matrix5. However, a significant decline in removal efficiency to 97.8% was observed at an initial Cr (VI) concentration of 100 mg/L, possibly due to the saturation of adsorption sites69. Moreover, elongating the contact time from 60 to 90 min resulted in a progressive improvement in removal efficiency from 99.09 to 99.3%. Extending contact time further to 120 min exhibited a marginal increase in removal efficiency to 99.8%, suggesting continued adsorption kinetics leading to saturation equilibrium70.

Figure 7
figure 7

The interaction effect of initial Cr (VI) concentration and contact time (BC).

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The interaction effect of contact time and adsorbent dose

Exploring the interplay between contact time and adsorbent dose, a fixed pH (6) and initial Cr (VI) concentration (70 mg/L) were maintained, while contact time and adsorbent dose were varied from 60 to 120 min and 0.5–1 g/200 mL respectively as shown in Fig. 8. Remarkably, an increase in contact time from 60 to 90 min yielded a notable enhancement in Cr (VI) removal efficiency from 99.09 to 99.3%. This enhancement could be attributed to prolonged exposure facilitating greater interaction between Cr (VI) ions and adsorbent sites71. Further extension of contact time to 120 min exhibited a slight elevation in removal efficiency to 99.89%, indicative of nearing adsorption equilibrium. Conversely, augmenting the adsorbent dose from 0.5 to 1 g/200 mL resulted in a substantial enhancement in removal efficiency from 98.7 to 99.3%. This enhancement may be attributed to increased surface area and availability of active sites, accommodating greater Cr (VI) adsorption72.

Figure 8
figure 8

The Interaction effect of contact time and adsorbent dose (CD).

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Process optimization

To optimize the process parameters, a numerical optimization strategy was employed using Design Expert software. The optimization aimed to refine the process conditions by minimizing the adsorbent dose and contact time while simultaneously maximizing the initial Cr (VI) concentration and removal efficiency. The desirability function was applied to determine the optimal conditions, with pH constrained within the experimental range of 3–9. After evaluating forty-eight potential solutions, the optimal conditions were identified, achieving a desirability score of 0.778 as illustrated in Fig. 9. The optimized parameters were determined to be a pH of 5.84, an initial Cr (VI) concentration of 88.94 mg/L, a contact time of 60.00 min, and an adsorbent dose of 0.52 g/200 mL. Under these conditions, the predicted removal efficiency of Cr (VI) reached 97.4606%, demonstrating the effectiveness of the optimization strategy.

Figure 9
figure 9

Process optimization.

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Adsorption isotherm

The investigation into the adsorption mechanism involved the application of nonlinear Langmuir, Freundlich, and Temkin isotherm models. Among these, the Langmuir isotherm exhibited the highest degree of conformity with the experimental data, evidenced by a correlation coefficient (R2) of 0.92836, as outlined in Table 5. This finding underscores the propensity for adsorption to occur in a monolayer fashion, suggesting homogeneity across the adsorption surface73. Furthermore, the separation constant (RL = 0.9874) indicates a favorable adsorption process. Such outcomes affirm the suitability of the Langmuir model in elucidating the adsorption dynamics under scrutiny.

Table 5 Adsorption equilibrium models.
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Adsorption kinetics and interaparticle diffusion

The removal rate of solids within the solid-solute interface is governed by adsorption kinetics. As delineated in Table 6, the experimental data aligns most closely with the pseudo-second-order model, boasting a correlation coefficient (R2) of 0.9880. This outcome suggests that the kinetics of the adsorption process are predominantly influenced by chemosorption phenomena74,75. To further support the fit of the kinetics to the pseudo-second-order model, we examined the compatibility between the theoretically calculated Qt values (from the pseudo-second-order model) and the experimental Qe values. The theoretical Qt values, derived from the pseudo-second-order model, closely match the experimental Qe values, demonstrating a strong agreement between the model predictions and experimental observations. This congruence reinforces the validity of the pseudo-second-order model in describing the adsorption kinetics in our system.

Table 6 Adsorption kinetics and intraparticle diffusion models.
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For the investigation of the diffusion mechanism, the Weber-Morris intraparticle diffusion model was employed. In cases where the regression line of a plot depicting qt against t0.5 intersects the origin, intraparticle diffusion stands as the sole rate-controlling step. However, in this study, the graph did not intersect the origin, indicating a non-zero intercept (C). Consequently, it is postulated that, in addition to intragranular diffusion, factors such as liquid film diffusion, surface adsorption, and other extragranular diffusion mechanisms may contribute to the rate-controlling step.

Table 7 illustrates the comparison of the Nano adsorbent’s efficacy in removing Cr (VI) with findings from prior studies. Upon reviewing the removal efficiencies of various nanocomposites for Cr (VI) removal, it is evident that the composite developed in this study, RAAC/AABC/GO/Fe3O4 nanocomposite, demonstrates exceptional performance. With a removal efficiency of 99.91%, it stands shoulder to shoulder with the highest-performing composite listed, the Peganum Harmala seed-driven activated carbon/zero-valent iron nanoparticles (GnZVI) composite. The incorporation of multiple components, including activated carbon, clay, graphene oxide, and iron oxide, likely enhances its effectiveness through diverse mechanisms for adsorption and reduction of chromium ions. This suggests that the composite holds significant promise for practical applications in water treatment and environmental remediation, offering a compelling solution for Cr (VI) removal.

Table 7 Comparison of this study with other studies.
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The reaction scheme for the adsorption of Cr (VI) ions onto the RAAC/AABC/GO/Fe₃O₄ nanocomposite is illustrated in Fig. 10. This figure depicts the detailed process of Cr(VI) ion interaction with the nanocomposite, incorporating insights from various characterization results including SEM, XRD, FTIR, and BET analyses. The schematic representation outlines the adsorption mechanism, highlighting the key interactions between Cr (VI) ions and the functional groups present on the RAAC/AABC/GO/Fe₃O₄ nanocomposite. This visual aid enhances the understanding of the adsorption process and complements the characterization data presented in the study.

Figure 10
figure 10

Reaction Scheme for the Adsorption of Cr (VI) Ions onto the RAAC/AABC/GO/Fe₃O₄ Nanocomposit.

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Conclusion

This study underscores the significant potential of the RAAC/AABC/GO/Fe3O4 nanocomposite as a highly effective and environmentally friendly adsorbent for chromium removal from aqueous solutions. Through detailed characterization, including SEM, XRD, FTIR, BET, and pHpzc analysis, the nanocomposite's suitability for this application was confirmed. The results demonstrated that the nanocomposite achieved a remarkable chromium removal efficiency of 99.91% under optimized conditions, with the adsorption process well-described by the Langmuir isotherm model, indicating a monolayer adsorption mechanism. The pseudo-second-order kinetics model further highlighted the efficacy of the nanocomposite in efficiently removing chromium.

Looking ahead, several key research directions could enhance the application and understanding of this nanocomposite. Future work should include a thermodynamic analysis to investigate how temperature affects the adsorption process and to derive important parameters such as enthalpy, entropy, and Gibbs free energy. This will provide deeper insights into the nature of the adsorption process. Additionally, studying the reusability and regeneration of the nanocomposite through multiple adsorption–desorption cycles will be crucial for assessing its long-term stability and practical application. Testing the nanocomposite with real industrial wastewater samples is also recommended to evaluate its performance in more complex conditions and to understand its effectiveness in real-world scenarios. Furthermore, scale-up studies are necessary to address practical implementation challenges and optimize performance in larger systems. Lastly, detailed mechanistic studies should be conducted to explore the molecular interactions between the nanocomposite and chromium ions.

By pursuing these future research directions, we aim to build on the promising results of this study and advance the development of sustainable solutions for chromium contamination in wastewater. This approach will support the creation of more effective, scalable, and practical methods for environmental remediation amidst ongoing industrial development.