Biofunctional hydrogel adsorbents for sustainable downstream dye purification and circular wastewater management

Research Highlights

• Integrated batch, column, and CFD studies optimized Malachite Green (MG) dye adsorption process.
• The removal efficiency of the MG dye reached around 90% by using AC@SA beads under optimal conditions.
• AC@SA beads with mesopores improved the electrostatic attraction of the MG dye.
• Langmuir and pseudo-second-order kinetics were found to fit the results better.
• A higher bed height caused an increase in the breakthrough time.
• CFD analysis showed stable fluid dynamics at 10 ml/min for maximum adsorption.

1 Introduction

The increase in industrialization and urbanization has resulted in an incessant flow of man-made organic substances into aquatic environments (Mishra et al., 2023). The most notable of these are the synthetic dyes that pose significant risks because of their complicated chemical structure, stability, and difficulty in natural degradation processes. Large amounts of effluent containing dyes are produced by textile, leather, paper, printing, pharmaceutical, and aquaculture industries (Arora et al., 2023; Al-Tohamy et al., 2022; Rai et al., 2023). Effluents without treatment before being released into natural water bodies can cause light obstruction, photosynthesis disruption, and toxic substance contamination of aquatic food chains (Berradi et al., 2019).

Malachite green (MG, IUPAC name: 4-[(4-dimethylaminophenyl)-phenyl-methyl]-N,N-dimethylaniline) is a synthetic cationic dye found in the textile industry as well as in the manufacturing industries, paper, leather, and dyeing industries. While widely used, MG has been reported as toxic and mutagenically active and as a hazardous pollutant, owing to its high level of toxicity and persistence in aquatic environments (Kleinhenz et al., 2019). The released untreated wastewater containing MG can decrease the amount of light that penetrates water bodies, suppress the photosynthetic activity of the environment, and put both aquatic organisms and people at risk (Srivastava, Sinha, and Roy, 2003). Based on the above mentioned, efficient removal of MG in such wastewater is a major environmental issue. It is necessary that malachite green be effectively removed so as not to contribute to the discharge of malachite green from industrial effluents. A few traditional wastewater treatment methods explored for the removal of dyes from wastewater that contain dye include coagulation and flocculation, membrane filtration, electrochemical oxidation, advanced oxidation, ion exchange and bio-degradation (Kumar, Devnani, and Pal, 2025; Arora et al., 2023; Keshta et al., 2024; Arora et al., 2025). However, these conventional processes have the drawback of high operational cost, incomplete oxidation and sludge formation of dyes, and difficulties in maintaining control through the whole process. Among the various treatment technologies mentioned above, adsorption remains one of the most promising techniques of MG removal owing to its simplicity, high efficiency, and ease of operation (Dutta et al., 2022; Mittal et al., 2022). Several adsorbents such as activated carbon, biochar, clay minerals, agricultural waste-derived materials, and metal oxides and nanocomposites have been studied for its MG adsorption purposes. The biofunctional hydrogels have recently been a focus owing to the attractive attributes of the three-dimensional porous network, high water retention capability, rich content of functional groups, and tunable surface character. Alginate, cellulose, chitosan, starch, and their composites-based hydrogels give better adsorption performance due to electrostatic interaction, hydrogen bonding, and pore filling mechanism. In addition, the addition of activated carbon, biochar, metal oxides, or nanomaterials into hydrogel matrices can markedly enhance adsorption potential, mechanical stability, and reusability. These benefits render biofunctional hydrogel-based adsorbents as a potential tool for the sustainable and effective removal of malachite green produced by the industry. Some factors that influence adsorption as dye removal technique, which includes the physical and chemical properties of adsorbent used. Some of which have surface area, porosity, charges, and functional groups. Recently, several studies have been conducted for the development of low-cost adsorbents based on agricultural waste products, biochar, clay minerals, and biopolymers (Qin et al., 2023; Guan et al., 2026; Huang et al., 2026; Liu, et al., 2025). There are numerous benefits associated with these materials, including their availability, environment-friendliness, and reusability. Clay minerals have a large surface area and high ion exchange capacity. The application of these materials is related to the removal of cationic dyes. Biopolymer, such as sodium alginate, can be used for preparing hydrogel beads because of its nontoxicity, biodegradability, and ability to form a stable structure owing to ionic interactions with divalent ions (Zhao et al., 2025). Combining clay and alginate results in composite hydrogel beads that can be advantageous for pollutant adsorption (Shah et al., 2024). The use of this material is appropriate for water purification purposes due to the easy recoverability of the product. Different materials have been utilized to adsorb malachite green over the past few years. Nano-bentonite clay composite materials have been used to absorb the dye in water. These materials are capable of absorbing 97% or more than 97% of the dye in water. The adsorption process of the material follows Langmuir isotherm model. Another material, almond shell bioadsorbent, has also been used to adsorb the dye in water. These materials are capable of absorbing 166 mg/g of the dye in water (Ighalo et al., 2025). These materials provide many benefits, including their availability, environmental friendliness, and regeneration. Clay minerals provide a large surface area and ion exchange capacity. These materials can be utilized for the adsorption of cationic dyes. The biopolymer that is sodium alginate can be utilized for hydrogel bead preparation. Sodium alginate has non-toxicity and biodegradability characteristics. Sodium alginate has a capability of developing a robust network structure through ionic interactions with divalent ions (Abourehab et al., 2022). The blend of clay and alginate results into the formation of a composite hydrogel bead, which will be advantageous in absorbing the pollutants. The advantage of using this material will be ease of recovering from water during water treatment. There have been numerous materials used in removing the malachite green from water in recent times. Nano-bentonite clay composite materials have been used to absorb this type of dye from the water (De Queiroga et al., 2019). These materials have been seen to be capable of removing over 97% of this particular dye from water. The ability of this material to remove the dye has shown that it follows the Langmuir isotherm model. Another material that has been used to absorb this dye from the water is almond shell bioadsorbents. Moreover, research into using soils and minerals as adsorbents for dye removal could also provide a method of water treatment on a larger scale. Despite the above-mentioned materials used as adsorbents, most research is based on batch adsorption techniques, and it has been found that batch adsorption provides very important data about adsorption equilibrium, kinetics, and thermodynamics of the adsorption process. On the contrary, in actual wastewater treatment processes, the adsorption column, or fixed-bed adsorption column, has been well-known to offer high efficiency in wastewater treatment due to its high adsorption capacity. This is because in a fixed-bed adsorption column, adsorbate and adsorbent can freely interact with each other. In general, adsorption capability and effectiveness of the fixed-bed adsorption column can be assessed based on the breakthrough curve that plots the variation of concentration of substances as a function of time. For example, mathematical models such as the Thomas, Yoon-Nelson, Adams-Bohart, and Clark models can be applied in effective interpretation of the breakthrough curve characteristics and column behavior for scaling-up to industrial-level operation (Hu et al., 2020). In relation to sustainability, it is obvious that the development of an efficient method for dye removal is highly correlated with the attainment of United Nations Sustainable Development Goals (SDGs). First, SDG-6 which refers to “Clean Water and Sanitation” emphasizes on the reduction of water pollution and improvement of wastewater management technologies. Conversely, SDG-12 which relates to “Responsible Consumption and Production” highlights the need for sustainable materials that not only decrease consumption rates but are more environmentally friendly. Therefore, regarding this, it is apparent that the development of sustainable adsorbent materials, such as clay-alginate composites, is essential for meeting the mentioned sustainability objectives and especially those related to wastewater management technologies.

In this paper, a study is done on the removal of malachite green dye from aqueous media via an activated clay-sodium alginate composite adsorbent material. The composite beads were prepared through the process of ionic cross-linking, which led to the formation of mechanically strong hydrogel beads. The adsorbent material was characterized via several methods, namely, XRD, FTIR, BET, and zeta potential measurements. The batch adsorption experiment was done to determine the effects of several parameters such as adsorbent dosage, agitation rate, pH value, and dye concentration. The adsorption equilibrium and kinetics for the adsorption of the dye onto the AC@SA beads were determined through several isotherm and kinetic models. Moreover, the adsorption kinetics was determined for the AC@SA beads via the column adsorption method and the breakthrough curves were determined via several adsorption kinetic models, namely, Thomas, Yoon-Nelson, and Clark models. The findings from this study are useful in understanding the processes involved in the mechanism and behavior of clay-alginate beads in dye removal. The research can be used in developing efficient adsorbents that can be applied in industry to treat effluent water, addressing the worldwide challenge of managing clean water resources and adopting environmentally friendly production processes. Despite a thorough examination of the adsorption properties of clay-alginate composite systems and activated clay adsorbents for removing dyes from water media, the majority of works have only considered the effect of adsorption equilibrium and kinetics in batch mode. Only a few studies have examined the effects of the dynamics of hydrodynamics in reactors on the adsorption process in continuous mode. For this reason, the present work aims to combine the experimental studies on adsorption processes and hydrodynamics analysis based on CFD.

2 Materials and methods

2.1 Development of adsorbent material

To make clay extremely reactive on the surface, it was heat-conditioned at 500 °C. In this instance, it functioned as the primary adsorbent. 3g of heated activated clay and 300 mg of sodium alginate were carefully combined with 10 ml of ultrapure water to create a spherical hydrogel composite material. The mixture was continually stirred at 200 rpm to achieve a uniform dispersion. To initiate instant ionic cross-linking, the combined solution was then dripped via a 1 ml pipette into a 2 ml calcium chloride solution. The hydrogel beads developed as a result of quick gelation made possible by contact with the calcium chloride solution. Following preparation, the beads were given a predetermined amount of time to cure in the cross-linking medium. To get rid of any extra calcium ions that might have been there, the beads were repeatedly cleaned with deionized water.

2.2 Characterisation of sorbent beads

To characterize the structure and the crystallinity of the AC@SA sorbent beads, X-ray diffraction (XRD) study was carried out using a Bruker X-ray diffractometer fitted with Cu Kα (λ = 1.54 Å) radiation. The XRD machine was run at 40 kV and 15 mA in 2θ range of 10°–90°, with a speed of 2° min^-1. The functional groups existing on the surface of the sorbent beads were characterized using Fourier transform infrared (FTIR) spectroscopy technique using PerkinElmer Spectrum Two in attenuated total reflectance (ATR) mode, where the number of scans and resolution were set at 32 and 4 cm^-1, respectively, in the wavenumber range of 400–4000 cm^-1. Textural properties, such as surface area, pore size, and volume, were analyzed by N₂ adsorption-desorption isotherms using Brunauer–Emmett–Teller (BET) equation at 77 K by using Quantachrome Autosorb iQ-x. The zeta potential (measured using the Anton Paar instruments) was used to evaluate the behavior of the surface charge on the beads in aqueous medium at pH 7.

2.3 Batch experiment

The batch adsorption tests were performed by mixing a fixed quantity of AC@SA bead samples with 50 ml of malachite green (MG) solution having an initial concentration of 10 mg L^-1 in a conical flask. In order to enhance the effectiveness of the process, some operational conditions were manipulated. The samples were withdrawn at equal time intervals of 15 min and the absorbance of the samples were measured at a wavelength of 617 nm, which is the specific wavelength for MG, using Agilent Cary 60 UV-Vis spectrophotometer.

The percentage removal of MG dye, or adsorption efficiency (A_e%), was calculated using Equation 1. The dye concentration at any given time (C_t) was derived from the absorbance data using Equation 2, and the adsorption capacity at time t (q_t) was estimated using Equation 3:

A e % = C 0 − C i C 0 × 100 (1)

C t = A t A 0 × C 0 (2)

q t = V C 0 − C t C t (3)

Where A_e% represents the adsorption efficiency, A _*0* is the initial absorbance of the dye solution, A _*t* is the absorbance at time t, C _*0* and C _*t* denote the initial and time-specific dye concentrations respectively, q _*t* is the adsorption capacity at time t, V is the volume of the dye solution (L), and m is the mass of the sorbent (g). The equilibrium dye concentration (C _*e*) and the corresponding adsorption capacity (q_*e*) were obtained from the final values of C _*t* and q _*t*.

2.4 Column studies

A continuous flow fixed-bed column was used to estimate the adsorption efficiency of AC@SA beads. A vertical glass column with an overall length of 60 cm and a diameter of 2 cm made up the experimental setup. While a non-adsorbent glass wool bed was integrated into the column bottom to prevent bead loss or displacement, the beads were put into the column to establish the fixed bed of beads. A Cole-Parmer peristaltic pump, which could pump fluids at flow rates ranging from 0.5 to 500 ml/min, was used to deliver the dye solution into the column via its top end. The primary operational factors were changed in order to assess the adsorption efficiency of beads under various operating situations. These included flow rate (5–15 ml min^-1), initial MG concentration (3–7 mg L^-1), and bed height (6–14 cm). The pH of the dye solution was kept constant at 7 for every run. Samples of the dye solution that emerged from the column were gathered into a reservoir, and the Agilent Cary 60 UV-Vis Spectrophotometer’s characteristic absorbance was used to calculate the samples’ residual MG content.

2.5 Breakthrough profile

Adsorption parameters that led to optimal adsorption were established by manipulating selected operating parameters such as bed height (Z), initial dye concentration (C₀), and volumetric flow rate (Q). The breakthrough curve was prepared by plotting effluent versus influent concentration ratio (C_t/C₀) against time (min). This resulted in an area underneath the breakthrough curve proportional to the total adsorption capacity. This information is highly instrumental in estimating the effectiveness of the column operation and optimization thereof. Breakthrough curves are very important in understanding the behavior of the column during its operation for longer periods of time. It is therefore very significant in evaluating critical properties of the column regarding hydrodynamics and thermodynamics.

Quantitative assessment of the breakthrough performance was conducted using the following theoretical and empirical equations (Equations 4–9):

V eff = Q . t total (4)

q total = Q . A . C 0 1000 = Q . C 0 1000 ∫ t = 0 t total C ad . dt (5)

m total = C 0 Q t total 1000 (6)

Removal % = q total m total × 1000 (7)

q e = q total X (8)

C e = m total − q total V eff × 1000 (9)

Where, V_eff is the total volume of effluent (ml), Q is the flow rate (ml min^-1), t_total is the total operational time (min), q_total is the total amount of adsorbed dye (mg), C_ad is the concentration of adsorbed dye (mg L^-1), C₀ is the influent dye concentration (mg L^-1), m_total is the total mass of dye introduced (mg), Removal (%) indicates the percentage of dye removed, q_e is the equilibrium adsorption capacity (mg g^-1), X is the mass of adsorbent packed in the column (g), and C_e is the equilibrium dye concentration in the effluent (mg L^-1).

2.6 PZC studies

PZC is a fundamental physicochemical property that provides information on the pH at which the total surface charge of the adsorbent becomes equal to zero. This is of great importance for adsorption studies because it impacts the electrostatic forces that develop between the adsorbent surface and the adsorbed ions. In the case of determining the PZC of AC@SA beads, the material had first to be ground into fine particles to facilitate mixing. Then, 30 mg of the adsorbent was added to nine conical flasks containing solutions of different pH levels (from 2 to 10).

The suspensions were agitated in an orbital shaker at 120 RPM for 24 h until equilibrium was attained. After that, the final pH of each suspension was determined. The pH difference (ΔpH = final pH–initial pH) of each sample was determined, and it was plotted against their initial pH values. The pH at which the value of ΔpH is zero indicates the point of zero charge (PZC), where the surface has no net charge. This is referred to as the pH drift method, and it is commonly used because of its high accuracy.

2.7 Isotherm study

Adsorption isotherms help to explain the equilibrium relationship between adsorbate molecules and the surface of the adsorbent, thus providing important information about the physical nature of adsorption processes (Kalam et al., 2021). Adsorption isotherms like Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich (D–R) adsorption isotherms have been used extensively to explain the equilibrium adsorption process. According to the Langmuir adsorption theory, adsorption occurs at the identical potential energy sites without interaction between each other and assumes monomolecular layer formation on the surface. On the other hand, the Freundlich equation allows adsorption over heterogenous sites and permits the formation of multilayer on the surface. On the other hand, the D-R isotherm equation distinguishes between physisorption and chemisorption mechanisms through calculating the mean energy of adsorption, based on the nature of the process. The data obtained experimentally was subjected to linear and nonlinear forms of these isotherms, while their corresponding parameters were estimated using regression analysis. The values obtained from the isotherm constants indicate surface inhomogeneities, energy distributions, and adsorption capacity, hence forming the basis for the assessment of the efficiency of the process and its viability.

2.8 Kinetic models for batch and column adsorption experiments

Adsorption kinetics investigations can be considered an important instrument which is aimed at revealing the kinetics of interactions of adsorbing molecules with the surface of the adsorbent up to establishing dynamic equilibrium. Moreover, this type of studies help determine the controlling stages and efficiencies of adsorption and whether it is possible to apply the process in practical systems of water treatment. Different types of kinetic equations are used depending on the nature of molecules involved in the reaction. Thus, the pseudo-first order equation implies a direct dependence of the speed of adsorption reactions from the number of vacant positions, whereas the pseudo-second order kinetics indicates that adsorption takes place via chemisorption when sharing or exchanging electrons occurs between the sorbate and sorbent (Wang et al., 2024). Finally, the intraparticle diffusion equation shows how the mass transfer occurs, including two stages - external and internal mass transfers. On the other hand, Elovich equation is often used in cases where the activation energy is dependent on the surface coverage (Cerofolini, G. 1996). This is because such a case involves a non-homogeneous surface and a path of absorption that cannot be well explained by either the first or the second order reaction. In this study, the kinetic data were fitted to both models and rate constants obtained from linearizing their equations.

Dynamic column adsorption models like Thomas, Adams-Bohart, Clark, and Yoon-Nelson models have also been used to determine fixed bed breakthrough characteristics. The Thomas model, which is developed using the concept of kinetics based on Langmuir theory of plug flow conditions without considering any dispersion, assumes a completely reversible interaction between the adsorbent and adsorbate. The Adams-Bohart model developed using the concept of surface reaction rate takes into account the breakthrough process by considering that equilibrium conditions are progressively attained. On the other hand, the Clark model incorporates mass transfer dynamics together with the Freundlich isotherm equation, hence making it possible to estimate parameters like K_f and 1/n in non-linear adsorption processes. In its simplicity, the Yoon-Nelson model establishes an association between breakthrough time and retention probability of the adsorbate. Evaluation of these dynamic models provides a complete picture of hydrodynamic performance and adsorption front migration. The constants generated from the model equations have been put in tabular form for comparison purposes.

2.9 CFD methodology

An overview of the numerical analysis of the flow dynamics within the packed bed reactor has been done. FLUENT 19.2, for simulating the system has been used. FLUENT software uses the philosophy of the finite element method (FEM) for solving the flow equations. The process involving in FLUENT 19.2 has been presented using a flowchart in Figure 1. The packed bed is a channel reactor of length 50 cm and 2 cm width that is subdivided into 10,000 structured grids, as illustrated in Figure 1a. On the other hand, the numerical domain is subject to a constant velocity at the inlet, zero pressure at the outlet, and the no-slip boundary condition at the wall. Moreover, the governing equations for controlling the flow dynamics are the incompressible Navier-Stokes equation (see Figure 1b), where ρ and μ represent the density and the viscosity of the fluid, respectively, while u stands for the velocity vector. Finally, the adsorption reaction has been considered by introducing the nonlinear Temkin isotherm model (see Figure 1c). These equations are simultaneously solved in the SIMPLE method to calculate pressure velocity, and a two-fluid model is chosen for two-fluid flow interaction. As presented in Figure 1d, the material utilized for this work is a man-made colorant of malachite green oxide dye, with a molecular weight of 927 amu, flowing inside the channel and passing through the packed bed of clay bead adsorbent material with a density of 848.86 kg m^-3. The total height of the bed remains constant, i.e., 10 cm, and the numerical analyses are performed for three distinct inlet flow velocities; 10 ml min^-1, 30 ml/min, and 50 ml min^-1. Finally, the outcomes of these computational analyses are evaluated based on some output variables, i.e., water velocity, turbulent kinetic energy, solid volume fraction, and eddy viscosity, as shown in Figure 1e. The complete discussion is elaborated in subsequent sections.

3 Results and discussions

3.1 Sorbent characterizations

Figure 2a presents an X-ray diffraction (XRD) pattern of the synthesized AC@SA beads, which shows a heterogeneous structure comprising crystalline and amorphous parts. A well-marked and highly intense peak in the vicinity of two theta 26.6 corresponds to the 101 plane of quartz, indicating the domination of crystalline silicon in the clay matrix. Alongside these peaks, additional weaker peaks spread across the scan angle of 10–80 can be associated with other silicate compounds or minerals that are naturally present in the clay. The somewhat higher baseline in the area lacking a clear peak suggests the existence of the amorphous phase coming from the sodium alginate, a polymer responsible for gel formation and bead molding (Wang and Lu, 2023). Being biopolymeric, sodium alginate can contribute to forming the amorphous structure in the sample. Such a combination of the crystalline silica structure and amorphous binder may prove effective in wastewater purification due to providing a mechanically stable porous material required for efficient dye adsorption in a continuously flowing solution.

The FTIR spectroscopic analyses before and after MG adsorption onto the AC@SA beads have shed light on the adsorption processes involved (Figure 2b). In the FTIR spectrum of beads prior to MG adsorption, characteristic absorption peaks were detected at 516.2, 689.6, 773.4, and 823.7 cm^-1, representing Si–O bending vibrations, metal-oxygen bond, and out-of-plane aromatic C–H deformations. In post-adsorption FTIR spectra, slight changes were seen, including shifts from 689.6 to 691.4 cm^-1 and 823.7 to 821.9 cm^-1, implying both physical and chemical bonding between MG and active sites on the surfaces of beads. Also, both spectra had strong peaks in the region of 1422.0–1600.9 cm^-1 representing C=C stretching vibrations of aromatic rings and C=N vibrations; hence, the existence of MG’s aromatic structure was confirmed by its peaks in the spectra. The appearance of a new peak at 1000.8 cm^-1 in the adsorbed MG FTIR spectrum, missing in the initial FTIR spectrum, confirms the retention of dye. Finally, the presence of a strong peak at 2359.4 cm^-1 common in both FTIR spectra could represent overtone vibrations and other adsorbed structures.

From the BET nitrogen adsorption-desorption isotherm of AC@SA beads presented in Figure 2c, it can be deduced that it follows Type IV isotherm and features a hysteresis loop, demonstrating that the beads are mesoporous. This conclusion is supported by the slow increase in adsorption volume and rapid increase around the point of P/P₀ = 1.0, showing well-developed mesopores. As seen from the pore size distribution (BJH) shown in Figure 2d, most of the pores have sizes between 3 and 10 nm, with the highest concentration at 4 nm, implying a uniformly distributed mesoporous system. This information agrees with the results of XRD spectroscopy, where both crystalline and amorphous phases were detected because of quartz and sodium alginate content. Porous properties support the outcomes of FTIR spectroscopy, where functional groups were involved in the interaction with dyes using electrostatic attraction, hydrogen bonding, and π-π interactions.

The zeta potential analysis (Figure 2e) showed that the activated AC@SA beads had an averaged zeta potential of −8.0 mV, and that the peak (mode) of zeta potential distribution was −5.56 mV. The mean value is the characteristic charge on the material and signifies that the adsorbent has a small negative surface at near-neutral pH. It is this negatively charged surface that encourages the adsorption of the cationic dye like malachite green by an electrostatic attraction. The value of −5.56 mV reflected in the zeta potential distribution does not correspond to the average value, instead being the charge of the particle present more often, which can help to account for the discrepancy between the figure and the manuscript. Moreover, the point of zero charge (PZC), based on the ΔpH versus initial pH plot (Figure 2f), was 7.22. Below this pH, the adsorbent surface remains positively charged, promoting cationic adsorption through additional interactions, while above pH 7.22 the surface becomes increasingly negative, which greatly influences the adsorption mechanism of the adsorbent.

3.2 Effect of process parameters for batch study

Optimization plays an important role in adsorption study because it will significantly affect the effectiveness and feasibility of the process. In this case, the One Variable at a Time (OVAT) method was used for the optimization process by analyzing the effect of certain factors like agitation rate, adsorbent amount, dye concentration, pH level, and temperature towards the process efficiency. Through this method, the effect of one variable can be examined by changing the value of that particular factor while maintaining other factors’ values at a constant level. Though this method does not consider the possible interactions between those factors, it is effective for preliminary process optimization since it is relatively easy to apply compared to other methods such as statistical method.

The optimization of the stirring rate was done using a series of experiments by fixing the dose of AC@SA adsorbent as 50 g L^-1, concentration of dye as 3 mg/L, pH as 7, and varying the stirring rates from 50 to 250 rpm. With an increase in the stirring rate, there is an increase in the adsorption efficiency up to 250 rpm, as seen in Figure 3a. At low stirring rates like 50 rpm and 100 rpm, there was a restriction in mass transfer between dye molecules and the adsorbent, leading to lesser adsorption. However, beyond the speed of 250 rpm, there were no more improvements observed. The highest dye removal efficiency was noted when the speed was set at 150 rpm. With consideration to both adsorption efficiency and energy use efficiency, the optimal stirring rate was optimized and kept constant at 150 rpm. Although high agitation rate enhanced the process, it caused greater mechanical strain and energy usage. Therefore, high agitation is not practical for continuous processes (Gilbert et al., 2006). However, 150 rpm was found to provide efficient mass transfer with enough dye removal without excessive agitation and mechanical stress. Hence, this optimal condition was chosen. Figure 3b shows that an increased amount of AC@SA dosage from 10 g L^-1–50 g L^-1 has greatly improved the removal efficiency of the dye. Increased amounts of AC@SA might have made greater available active sites for malachite green adsorption, hence better adsorption efficiency. However, at 50 g L^-1 at 180 min, there was no substantial improvement in the Ae (%) since the number of available molecules is saturated at higher adsorbent doses. Therefore, 50 g L^-1 was the optimal amount of the AC@SA beads used in the MG dye removal. From Figure 3c, it can be seen that the efficiency of adsorption (A_e (%)) varies inversely with an increase in initial dye concentration varying from 3 mg L^-1–11 mg L^-1. In case of lower dye concentration (i.e., at 3 and 5 mg/L), the rate of uptake of dye molecules by the adsorbent material is very fast, and it reaches up to about 90% efficiency within 120–150 min. This happens because there are many active sites for adsorption, which are much more than the number of dye molecules. On increasing the dye concentration up to 7, 9, especially 11 mg L^-1, the graphs tend to flatten out. The maximum value of A_e (%) for 11 mg L^-1 never exceeds 80% even after 180 min. This occurs because of the exhaustion of active adsorption sites, i.e., the adsorption sites become saturated by dye molecules, and further adsorption does not take place efficiently (Santos et al., 2022). Moreover, higher dye concentrations can also cause greater competition between the dye molecules for the active sites as well as increase the concentration boundary layer thickness, thus slowing the mass transfer rate. The values of adsorption efficiencies from 3 mg L^-1–5 mg L^-1 also give almost equal results, where qe values were considered while fixing the initial dye concentrations at 5 mg L^-1. It can be seen from Figure 3d that there is a significant effect of pH on the adsorption of the dyes. The adsorption capacity was relatively low at the acidic pH values such as 3 and 5, which may be attributed to protonation on the adsorbent surface, causing a decreased affinity towards the cationic dyes. The maximum value of A_e (%) was noted at pH 11, where the surface carries a high negative charge, leading to a higher electrostatic interaction between the surface and the positively charged MG dye molecules. The pH value of the dye, i.e., MG dye solution, is neutral; thus, the PZC value of the AC@SA beads (7.22) is near it. All adsorption studies were performed under neutral pH conditions. Moreover, as seen in Figure 3e, temperature plays a very important role in adsorption efficiency (A_e%). The efficiency is rapidly increased as temperature increases from 20 °C to 50 °C due to the improved molecular mobility and decrease in solution viscosity and the rate of mass transport onto the adsorbent surface. The curves reach a plateau when temperature exceeds 40 °C since the adsorption sites become saturated and equilibrium is reached. Moreover, the interaction between malachite green and AC@SA may be weakened at a higher temperature, implying that the adsorption is not highly endothermic. Hence, the higher temperature will not improve the adsorption efficiency but may negatively affect it. Considering all the aspects mentioned above, it is necessary to choose an optimal temperature within the range of 30 °C–40 °C.

3.3 Effect of process parameter for column study

It is evident from Figure 4a that an increase in bed height from 6 cm to 14 cm caused a remarkable improvement in the adsorption capacity. With lower bed heights, the dye solution quickly saturated the available active sites in the adsorbent, resulting in early breakthroughs and shorter exhaustion periods. However, with higher bed heights, a higher amount of adsorbent mass and available active binding sites was present, thereby providing more opportunities for the dye molecules to interact with the surface of the adsorbent (Aziz et al., 2025). As a consequence, a longer period was required before breakthrough occurred, resulting in a larger plateau region and better dye adsorption performance.

Influence of Initial Concentration of Dye on Column Adsorption Behavior of AC@SA Beads was investigated using 3 mg L^-1, 5 mg L^-1, and 7 mg L^-1 of MG dye solution concentrations (Figure 4b). According to breakthrough curves shown, there is significant dependency on feed concentration. In case of lowest concentration of 3 mg/L of dye, the breakthrough point (C/C₀ = 0.1) appears approximately after 20 min, while the adsorbent bed exhausted after 36 min (C/C₀ = 1.0). Longer MTZ shows more efficient use of adsorbent, which might be caused by reduced competition between dye molecules for active binding sites on beads (Al-Degs et al., 2008). At higher concentration of 5 mg L^-1 of dye, the breakthrough time decreased to 15 min, and exhaustion of beads occurs after 32 min. When the dye concentration reaches 7 mg L^-1, breakthrough happens within 10 min, and full bed exhaustion after 27 min. The fast increase in the C/C₀ profile at high concentrations is due to the higher concentration gradient, which provides a higher force for mass transfer, yet also results in a shorter time for filling up the available adsorption sites. While increasing the concentration improves the amount of adsorbed dye due to its abundance, it negatively impacts the performance of the adsorption column since it will have less time before breakthrough (Rápó and Tonk, 2021). Consequently, based on both effectiveness and sustainability criteria, it can be concluded that 3 mg L^-1 is the optimum concentration of dye for an AC@SA column.

As shown in Figure 4c, the increase in flow rate from 10 ml min^-1–50 ml min^-1 caused an observable reduction in adsorption effectiveness. The lower flow rate causes the solution to flow slower in the column, thereby giving more time to interact with the adsorbent. In this case, there was a delay in breakthrough and more adsorption efficiency. When the flow rate is increased, the solution flows quickly inside the column. Thus, the interaction time between the dye and the adsorbent is shortened, and the dye molecules did not get enough time to diffuse inside the pore structure or the active site. This results in quick breakthrough and low adsorption effectiveness (Tan et al., 2023). Hence, for the AC@SA adsorbent, the best flow rate is 10 ml/min. Table 1 shows the variation of operating conditions of column study.

TABLE 1. Effect of variation in bed height, solution flow, Dye concentration of AC@SA beads on process.

3.4 Isotherms, kinetic models and CFD analysis

3.4.1 Batch study

The adsorption behavior of AC@SA beads was thoroughly investigated using the canonical adsorption models, as provided by the experimental results as well as graphs. It was observed from temporal adsorption behavior (Figure 2) that there were fast uptake behaviors at first stages due to active site availability, which later resulted in a diffusion-limited adsorption equilibrium stage. Both pseudo-first order (Figure 5a) and pseudo-second-order kinetic (Figure 5b) models had very good fits (R ² ≈ 0.98). It was observed that the pseudo-second-order kinetics had better values of the rate constant (K₂) than those of the former model. This indicates that chemical sorption by valence electron interaction mechanism is dominant here. Intra-particle diffusion behavior (Figure 5c), along with the Elovich kinetics (Figure 5d), highlights the multiple steps involved in adsorption behavior involving film diffusion and diffusion inside the pores, as well as the heterogeneous character of the interface (K_id, α, and β values).

From the equilibrium isotherms analyses conducted (Figure 6), the Langmuir equation (Figure 6a) (R ² = 0.99) was found to explain best the maximum sorbent capacity (qm) and binding energy (K_L) associated with dye molecules. This implies that the sorption process involves a homogenous monolayer coverage and the RL values obtained suggest favorable thermodynamics for the removal processes. On the other hand, Freundlich (Figure 6c) and Temkin (Figure 6b) equations (R ² = 0.94; R ² = 0.96) were supportive of the existence of binding energies and surface interactions which are heterogeneous in nature as depicted by constant values (n, K_f, B_t, K_t). From the analysis, it is clear that AC@SAs can be used effectively to remove dyes through a combination of monolayer chemisorption, physical adsorption on heterogeneous surface and mass transfer inside the particles.

3.4.2 Column study

The kinetics column performance of AC@SA beads were analyzed by Thomas, Yoon-Nelson, and Clark models based on the experimental breakthrough curves collected at different operating conditions. As shown in Figures 7a–c, the breakthrough profiles showed an S-shape trend, implying the gradual depletion of available sites as time elapses. The Thomas model provided an excellent fit with the experimental data set, showing high consistency with R ² values of 0.99, confirming its accuracy in describing the adsorption in monolayer fashion according to Langmuir theory under steady state. The Thomas rate constant (K_th) was found within the range of 0.22–0.33 ml mg^-1 min^-1, and the maximum adsorption capacity (q₀) varied from 9.80 to 21.28 mg g^-1. From the results, it can be concluded that an increase in bed height (14 cm) and influent concentration (7 mg L^-1) greatly improves adsorption performance. As illustrated in Figure 7a, the breakthrough period was observed to have increased from about 18 min at 6 cm to about 32 min at 14 cm, thus supporting a better adsorption capability with a larger adsorbent amount. For instance, as illustrated in Figure 7b, increasing the feed concentration from 3 to 7 mg L^-1 resulted in a more rapid increase in breakthrough curves with a higher K_th value (0.33 ml mg^-1 min^-1) and lower q₀ value at lower concentrations. In contrast, as shown in Figure 7c, an increment in flow rate from 10 to 50 ml min^-1 led to an earlier breakthrough and sharp concentration fronts. This phenomenon could be attributed to the reduction in retention time and lack of mass transfer equilibrium. Additionally, the Yoon-Nelson model exhibited a good fit (R ² = 0.99) with a kinetic constant (KYN = 0.22–0.33 min^-1) comparable to that of the Thomas model, suggesting that there is a proportional relationship between adsorbate breakthrough chance and adsorption rate. Similarly, the Clark model, with r values ranging between 0.24 and 0.49 min^-1 and A values between 5.95 and 2144.2, well represented the combined effect of Freundlich-type adsorption and mass transfer resistance. The positive correlation between the results of the experiments and the results obtained through simulation in the models indicates the strength of the kinetic models in predicting the dynamic properties of adsorption by the AC@SAs filled columns. The Thomas model was found to be more accurate in predicting the breakthrough phenomenon while designing the columns, whereas the other two models were successful in providing information about the adsorption kinetics of the composite adsorbent. Table 2 shows the kinetics data of column study. Table 3 shows the isoterm data of batch study.

TABLE 2. Summary data of column kinetic models.

TABLE 3. Summary data of adsorption isotherm and kinetic model.

3.4.3 CFD analysis

The CFD simulations were conducted using three inlet flow rates (10, 30, and 50 ml min^-1) while keeping the packed bed length at 10 cm. The main intention behind conducting these simulations was to determine the effects of hydrodynamics on the adsorption capacity quantitatively by calculating the velocity distribution, TKE, solid volume fraction, and eddy viscosity. With a flow rate of 10 ml min^-1 (Figure 8a), the velocity field was highly uniform throughout the packed bed, with the maximum velocity being about 6.49 × 10^-4 m s^-1. The level of turbulence kinetic energy was low and achieved a peak of 3.57 × 10⁻⁷ m² s^-2; this parameter was mainly high in the inlet and outlet regions. The maximum eddy viscosity was found to be 2.03 × 10⁻³ Pa s, meaning that there was little turbulence-induced momentum transfer within the packed bed. The solid volume fraction showed a relatively constant trend, maintaining its approximate value of 0.60. This means that the solid particles were not moved out of their places by the flow. At a flow rate of 30 ml min^-1 (Figure 8b), the highest fluid velocity became about 1.96 × 10⁻³ m s^-1, which is almost triple the value at a flow rate of 10 ml min^-1. While the total flow behavior remained fairly consistent, the areas with high velocities became more pronounced around the inlet and outlet sections. At a flow rate of 30 ml min^-1, the maximum TKE (3.56 × 10⁻⁷ m² s^-2) and eddy viscosity (2.01 × 10⁻³ Pa s) values remained almost constant when compared with those recorded at 10 ml min^-1, implying that turbulence formation was still limited. However, increased fluid velocity led to decreased residence time (Loll et al., 2023). The maximum value of the velocity was reached to be about 3.27 × 10⁻³ m s^-1 at the maximum flow rate of 50 ml min^-1 (Figure 8c). This represents nearly a five-fold increase compared to that at the flow rate of 10 ml min^-1. In terms of the velocity contours, there appeared to be preferential flow patterns and an increase in the velocity gradient, suggesting that the channeling effect becomes more pronounced. Though the values of the maximum TKE (3.67 × 10⁻⁷ m² s^-2) and eddy viscosity (2.00 × 10⁻³ Pa s) were only marginally higher than those at lower flow rates, due to increased flow momentum, non-uniformity in flow distribution and decreased solid–fluid contact became inevitable (Nichik et al., 2024). Based on the results obtained through CFD simulation, it can be concluded that flow rate is an important parameter influencing the hydrodynamics inside the adsorption column. Out of all conditions considered, 10 ml min^-1 exhibited the best hydrodynamics due to lower velocity, uniform flow profile, lack of turbulence generation, constant solid volume fraction, and appropriate residence time for fluid. This conclusion is also supported by the experiments, where the best adsorption result was observed under the lowest flow rate condition, giving the longest breakthrough time among all other parameters.

The results from the CFD analysis compared to the fixed-bed adsorption experiment show a good match in their qualitative aspect. In the adsorption experiment, the effect of changing the flow rate from 10 to 50 ml/min was an early breakthrough, as well as low adsorption efficiency due to decreased contact time of the dye solution with the adsorbent material. The same pattern was noted in the CFD analysis, where the maximum velocity value changed from 6.49 × 10^-4 to 3.27 × 10⁻³ m/s when increasing the flow rate. Preferential paths were more evident for high flow rates, which leads to shortened residence times and decreased mass transfer processes. In contrast, the 10 ml/min flow rate shows the most uniform flow profile, low velocity magnitude, steady solid volume fraction and least turbulence formation, which makes the interaction of the adsorbing and adsorbent materials effective.

4 Regeneration and reusability of AC@SA hydrogel beads

The regeneration and reuse of the AC@SA hydrogel beads were determined by five consecutive batch adsorption-desorption cycles with MG dye as its target pollutant. After each batch adsorption cycle, the discarded adsorbent was regenerated by desorbing the adsorbed dye and washing it with deionized water and subsequently reusing under same operating conditions. The obtained findings are shown in Figure 9. As shown in Figure 9, the beads regenerated with a very good regeneration process in a fractionate reduction of removal efficiency after the use. The removal efficiency decreased from 97.2% ± 0.8% in the first cycle to 96.4% ± 1.0%, 95.3% ± 1.1%, 93.9% ± 1.2%, and 92.8% ± 1.4% after the second, third, fourth and fifth cycles, respectively. The relatively small standard deviations indicate good experimental reproducibility and demonstrate the stability of the adsorption process during repeated regeneration cycles. The slight decline in adsorption performance observed after successive cycles may be attributed to several factors. The initial reason is because a significant fraction of MG molecules might be strongly attached to active functional groups of the hydrogel matrix, even after desorption has resulted in incomplete adsorption site regeneration. Second, repeated adsorption–desorption operations can induce partial pore blocking and incremental occupying of active binding sites, thus decreasing the adsorption centers available. Moreover, small structural rearrangements of alginate-based hydrogel network following repeated swelling and deswelling processes could reduce the available adsorption sites. However, due to these effects, the loss of removal efficiency remained less than 5% after five cycles-re-energized. Significantly, the hydrogel beads have maintained about 95.5% of their original adsorption performance after the fifth regeneration cycle, confirming their impressive structural integrity and stability toward degradation. The significant adsorption retention indicates the excellent stability of the activated carbon–sodium alginate network, and confirms that the adsorbent can pass through multiple operating cycles with no observable performance degradation. From a practical point of view, exceptional regeneration ability allows to greatly increase the economic value of the developed adsorbent so as to enhance the recycling efficiency as well as to decrease the rates of adsorbent replacement and decrease the secondary waste generation. This capacity of preserving high dye removal capability over multi-reuse cycles indicates that beads have strong prospects for long-term use in treatment applications, such as wastewater treatment systems. Thus, the regeneration study validates the excellent hydrogel adsorbent in removing MG, and it is also feasible for environmental remediation process, on environmental remediation application in practical and cost effective environment.

5 Cost analysis, regulatory frameworks and policy suggestion

A preliminary techno-economic evaluation was undertaken to assess the feasibility of using AC@SA hydrogel beads for the continuous treatment of wastewater. The current analysis does not concern the cost of adsorbent production as it considers the cost of treatment per volume of the effluent wastewater treated (Soni et al., 2020; Rai et al., 2023). A pilot adsorption system with 10 m³ day^-1 processing capacity over 300 days per year, with the total annual treatment capacity being 3000 m³, has been studied. The investment consisted of a capital cost of USD 5000 for adsorption columns, pumps, storage tanks, pipework, and instrumentation. The annual maintenance: electricity, regeneration chemicals, labour, machine labor was expected to cost USD 1800. To estimate the Annualized Capital Cost the Cost Recovery Factor (CRF) method was used with a 6% rate of interest and a 10 years life of the equipment (Panda et al., 2024). An estimated value was 0.136 for the calculated CRF. Annualized capital cost was estimated to be 0.227 USD m^-3 of treated wastewater and annualized operational cost was 0.60 USD m^-3. Thus, the total treatment cost was equal to 0.83 USD m^-3 treated wastewater (Table 4). The comparatively low treatment cost is due to the excellent regeneration performance of the AC@SA hydrogel beads, which maintained more than 95% of their original adsorption efficiency after five adsorption-desorption cycles. Besides, the naturally abundant clay and biodegradable sodium alginate contribute to this process sustainability and economics. The estimated treatment cost is a good estimate compared to some reported adsorption-based dye removal systems, which highlights the potentiality of AC@SA hydrogel beads as a low-cost and eco-friendly technology for high-throughput recovery of dye-containing industrial effluents.

TABLE 4. Detailed techno-economic analysis of the AC@SA hydrogel bead-based adsorption system for malachite green-contaminated wastewater treatment considering a 10 m³ day^-1 pilot-scale treatment facility.

The findings above have not only important consequences in terms of adsorption but also for the governance of the environment and industrial wastewater policy. MG is a triphenylmethane cationic dye used extensively in textile dyeing, aquaculture, and biological staining, has been identified as a persistent environmental hazard due to its mutagenic and carcinogenic properties. Nevertheless, a universally enforced discharge limit for MG in industrial effluents remains absent across most national and international regulatory frameworks. While the European Union’s Water Framework Directive (2000/60/EC) calls for member states to reach good ecological and chemical status in water bodies, there is no specified threshold concentration for MG. Likewise, the US Environmental Protection Agency (USEPA) effluent regulation of textile industries broadly regulates colour and biochemical oxygen demand (BOD) but does not specify MG-specific quantitative boundaries. World Health Organization (WHO) recommendations on drinking water quality establish limits for a broad range of chemical contaminants and toxicological characteristics, but their own guidelines do not specify a maximum contaminant level for MG. The South Asian countries which are home to a high proportion of global textile manufacturers, regulation of dye effluent discharge is not uniformly applied.

Moreover, this study directly contributes to a number of United Nations Sustainable Development Goals (SDGs). SDG-6 (Clean Water and Sanitation) calls for the elimination of water pollution and improvement of wastewater treatment technologies around the world. The AC@SA composite hydrogel bead system developed in this study demonstrates an inexpensive, scalable, and effective pathway to achieving this goal, especially in low- and middle-income economies, where advanced treatment facilities are scarce. SDG-12 (Responsible Consumption and Production) is fulfilled by working out an adsorbent containing naturally derived, biodegradable materials such as activated clay and sodium alginate, which minimizes the reliance on artificial chemical reagents. Additionally, SDG-9 (Industry, Innovation, and Infrastructure) is advanced by combining computational fluid dynamics (CFD) simulation with experimental column studies which provide a basis for innovative industrial wastewater treatment design based on data. Policymakers and funding bodies are recommended to consider this type of multi-SDG research as a priority for grants, technology transfer programmes and industrial adoption incentives.

Based on the experimental outcomes of this study, the following policy recommendations are proposed (Figure 10).

• Regulatory agencies should establish maximum permissible limits for MG concentration in effluents discharged by textile, aquaculture, pharmaceutical, and biological research industries. International bodies including UNEP, SAARC, and ASEAN should initiate coordinated efforts to establish unified MG discharge standards across textile-manufacturing nations. The absence of harmonised global thresholds currently allows industries to exploit regulatory disparities across borders, undermining collective water quality protection efforts.
• Governments should recognize adsorption-based treatment as a mainstream, large-scale wastewater management technology rather than a laboratory-confined process. Biodegradable composites derived from naturally abundant precursors address this directly, offering a circular, low-liability treatment solution. The AC@SA composite developed in this study meets all these criteria, and its demonstrated reusability and re-generability make it particularly well-suited for communal large-scale deployment across shared industrial effluent treatment systems.
• Environmental policy should actively promote the development and commercialization of biodegradable, low-cost adsorbent materials. The AC@SA composite beads developed in this study exemplify circular economy principles, activated clay sourced from abundant natural mineral deposits combined with sodium alginate derived from marine biomass results in a material with minimal environmental footprint.
• Malachite green has historically been used and in many regions continues to be illegally used as an antifungal agent in fish farming. Its accumulation in aquatic organisms poses a food safety risk to human consumers. The adsorbent developed in this study is capable of removing MG from aquaculture effluent streams under neutral pH conditions, making it directly applicable to on-site treatment systems at fish farms. Regulatory agencies overseeing aquaculture should mandate the installation of such adsorption-based effluent treatment units at licensed fish farming operations as a condition of licensing renewal, reducing the entry of MG residues into the broader aquatic ecosystem and the human food chain.

6 Conclusion

The present research proves the efficiency of AC@SA composite hydrogel bead synthesis and use as a potent sorbent for eliminating malachite green from aqueous solutions. From structural studies, it was evident that a mesoporous heterogeneous matrix was created that exhibited good surface chemistry with a point of zero charge of 7.22, which facilitated strong electrostatic interactions with the cationic dye molecules. Batch adsorption studies revealed that maximum removal efficiencies were observed under the following parameters: 50 g L^-1 dosing, pH = 7, and 150 rpm, yielding efficiencies of nearly 90% in 150 min. Adsorption was found to be better described using the Langmuir isotherm (R ² = 0.99), showing monomolecular layering, whereas second-order kinetic data fitted well (R ² = 0.98). In addition, the results obtained from the column studies have shown that an increase in the bed height has greatly improved the adsorption behavior, resulting in increased breakthrough times from 18 to 32 min. However, higher influent concentrations up to 7 mg L^-1 and flow rates up to 50 ml min^-1 have adversely affected column performance by reducing efficiency due to fast saturation. Among the models tested, the Thomas model had the best prediction capabilities (R ² ≈ 0.99) with adsorption capacities of 21.28 mg g^-1. Furthermore, the CFD simulation provided insights into the underlying mechanisms associated with reactor hydrodynamics by indicating that a flow rate of 10 ml min^-1 is sufficient to ensure uniform flow distribution, low eddy viscosity, and limited turbulence. The study strongly recommends the establishment of harmonized global MG discharge standards and policy-driven promotion of low-cost green adsorbents to ensure safer water management and circular wastewater treatment practices.

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Footnotes

Edited by: Arnab Mukherjee, Sidho Kanho Birsha University, India

Reviewed by: Charu Arora, Guru Ghasidas Vishwavidyalaya, India

Balasubramanian Sivasamy, KPR Institute of Engineering and Technology, India