Sol-gel synthesis of composite adsorbent coating from Prosopis juliflora–activated carbon for simultaneous adsorptive removal of Cd²⁺ and Cr₂O₇^2- from wastewater

The discharge of substantial quantities of valuable heavy metals, including cadmium and chromium, into global wastewater systems remains a pressing industrial concern. In industrial waste, chromium and cadmium can be found as natural deposits. However, both metals are exceedingly hazardous and they tend to accumulate in the environment (Gupta et al. 2021). Cadmium serves as a key constituent in various industrial sectors, including plating, battery manufacturing (specifically for cadmium–nickel batteries), phosphate-based fertilizer manufacturing, stabilizer formulation, and alloy development (Demim et al. 2013). According to Genchi et al. (2020), exposure to cadmium occurs through a number of pathways including consumption of contaminated water and food, inhalation, and smoking. These exposures have been linked to the development of chronic renal failure, cancer, as well as bone demineralization and fragility in both human and animal populations (Mahdi et al. 2021). Moreover, cadmium exposure has been associated with several symptoms, including vitamin D deficiency, respiratory-related ailments, and gastrointestinal disorders that result in the loss of red blood cells. These conditions can impede the proper functioning of calcium in both human and animal bodies (Filipič 2012; Genchi et al. 2020). Similarly, chromium is used in such industries as leather tanneries, electroplating, production of stainless steel, and preservation of wood (Nur-E-Alam et al. 2020). The most common forms of chromium found in wastewaters and the aquatic environment are Cr (III) and Cr (VI), with the latter being more harmful than Cr (III) compounds (Kinuthia et al. 2020) even though the degree to which chromium is mobile and toxic is predominantly determined by its physicochemical speciation (Tian et al. 2022). Cr (VI) causes alopecia, liver and kidney damage, skin irritation, and genotoxicity (Vaiopoulou & Gikas 2012). It is therefore imperative to eliminate these metals from industrial effluent prior to their introduction into aquatic environments, as their detrimental impacts on human health have been unequivocally demonstrated. Among the available wastewater treatment techniques, adsorption is currently the most frequently utilized method for water purification, because of its high pollutant removal efficiency, simplicity, and its potential to treat a large quantity of water in a semi-continuous system with acceptable costs (Somma et al. 2021).

Utilization of low-cost activated carbon prepared using locally available biomass has gained enormous attention in water and wastewater treatment over the past decade (Mohd Udaiyappan et al. 2017; Shaikh et al. 2018; Ukanwa et al. 2020; Hussain et al. 2021; Somma et al. 2021; Tian et al. 2022). Despite having a wide surface area that favors its capacity to adsorb a variety of water contaminants, powdered activated carbon (PAC) typically presents handling challenges, because it generally occurs in powder form (Shamsudin & Shahadat 2019). Specifically, the direct application of activated carbon in the fine-particle powdery form causes leaching-related adsorbent loss and also leads to the development of secondary pollutants in treated water/wastewater complicating the separation of pollutant-loaded adsorbent from the treated water or wastewater (Azha et al. 2017). For easy phase separation, the adsorbent material must be coated or immobilized onto a substrate or supporting material. After all, the use of supporting materials for adsorption is said to enhance the mechanical strength and binding efficiency of the obtained adsorbent (Arifin et al. 2013).

Prior research has examined the adsorption interaction of zwitterionic adsorbent coating using bentonite and zeolite as adsorbents. Acrylic polymer emulsion (APE) was utilized as a binder, while cotton cellulosic fabric served as the supporting material/substrate for dye removal from wastewater (Yi et al. 2012; Arifin et al. 2013; Azha et al. 2017; Shamsudin & Shahadat 2019). These investigations successfully avoided the need for separation techniques, such as centrifugation and filtering, to obtain the final treated effluent. As much as the immobilizing of the adsorbent on a substrate was successful, however, none of the previously cited works have investigated the use of such composite adsorbent materials specifically prepared from low-cost adsorbent (e.g. Prosopis juliflora) wood for simultaneous removal of heavy metal from wastewater.

Numerous studies have unanimously concluded that the Prosopis juliflora plant's drawbacks far exceed the benefits (Mehari 2015; Abdulahi et al. 2017; Azha et al. 2017; Tilahun et al. 2017; Azha & Ismail 2019; Azha et al. 2019). The studies have recommended an effective utilization of this invasive species as a means of eradicating it completely. To this end, various studies have been conducted to investigate the potential of the Prosopis juliflora plant as an adsorbent, which would be one way of effectively utilizing it (Rajput et al. 2012; Megersa et al. 2014; Gopal & Asaithambi 2015; Jain et al. 2016; Patnaik et al. 2017; Sivakumar et al. 2017; Yohannes 2017; Sanghavi & Ranga 2019; Shiferaw et al. 2019; Panigrahi & Santhoskumar 2020). However, such studies have not used Prosopis juliflora–PAC as an adsorbent in composite adsorbent material formation. This study focuses on the development of a cost-effective and eco-friendly solution for the removal of heavy metals from wastewater. It involves the preparation of a composite adsorbent coating (CAC) using PJAC through a sol-gel method. The CAC's efficacy in removing Cd²⁺ and ions, chosen as model pollutants, is evaluated and optimized within a batch adsorption setup employing a binary component system.

All chemicals utilized in this investigation were of analytical grade and were employed without additional purification. These chemicals included zinc chloride (ZnCl₂), sodium hydroxide (NaOH), cadmium chloride hydrate (CdCl₂⋅H₂O), potassium dichromate (K₂Cr₂O₇), and hydrochloric acid (HCl). The APE (Ecronova ^® RA 127) was supplied by Mallard Creek Polymers (MCP 2012).

Prosopis Juliflora wood material was collected from Kurkura in Awash, Afar Region, Ethiopia. A blade was used to cut away the external branches and spikes. The precursor was comprehensively cleaned with tap water and later rinsed using deionized (DI) water to remove soil, dust, and other particles of impurities. The clean wood biomass was then cut into pieces of approximately 1–2 cm and sun dried for a week.

The activated carbon used for this objective was prepared using pyrolysis method utilizing both chemical and thermal activation. First, the dried biomass was impregnated with a boiling solution of zinc chloride (ZnCl₂) using an impregnation ratio¹ of 1.8 for 2 h and soaked in the same zinc chloride solution for 24 h. After 24 h, the excess zinc chloride solution was decanted off and the biomass was air-dried after thorough washing with DI water. Subsequently, the material was placed in a muffle furnace, carbonized at 595 °C for 174 min to eliminate the volatile matter. The carbon was then thoroughly washed with DI water, oven dried at 40 °C for 24 h, powdered, and then activated in a muffle furnace at 800 °C for a period of 120 min at 10 °C/min heating rate to develop the porosity and surface area. Finally, the PAC was sieved through a 150 μm mesh and stored in air-tight plastic bags for characterization and subsequent CAC preparation.

In an attempt to obtain a high surface area and a stable surface for adsorption, a facile sol-gel method adapted from Azha & Ismail (2019) was used for the CAC preparation. PJAC, cotton cellulosic fiber (CCF), APE, and DI water were used in CAC fabrication.

The synthesis of the PJAC/APE-CCF adsorbent coating involved the blending of PJAC/APE/DI through mixing 2 mL of APE and 4 mL of DI water with different quantities (0.25, 0.50, and 0.75 g) of PJAC. The resulting slurry was mixed with a magnetic agitator (Model RT Power) for approximately 3 h to form a homogeneous solution. For every 15 cm × 5 cm (75 cm²) of CCF per given adsorbent dosage, 2 and 4 mL of APE and DI water were used, respectively. For instance, in case of a 0.25 g CAC, 2 mL of APE and 4 mL of DI water were used per 0.25 g of adsorbent on a 15 cm × 5 cm (75 cm²) CCF.

A brush was employed to spread the homogeneous slurry of APE/PJAC on both sides of the 75 cm² CCF. To prevent detachment of the adsorbent coating, the prepared PJAC/APE/CCF strips were subjected to drying at 70 °C for 5 h. The coated strips were then stored in air-tight bags for characterization and for use in further adsorption tests.

A CAC-powdered sample was prepared for both the Brunauer–Emmet–Teller (BET) surface area (S_BET) and particle size distribution (PSD) analyses. This involved mixing a known amount of APE, DI water, and adsorbate (adsorbate dosage) for 180 min until a homogeneous blend was obtained. Then the homogeneous mixture was subjected to drying for 7 h at 40°C in a vacuum oven (Townson + Mercer) under 300 mbar pressure. Subsequently, the desiccated material was pulverized into a fine powder and sieved through a 150 μm mesh for S_BET and PSD analyses.

The pore volume (cm³/g), surface area (m²/g), and pore size (125.3 Å) were determined through the utilization of the N₂ adsorption/desorption isotherm technique employing the BET method. The N₂ adsorption/desorption isotherms were obtained by conducting measurements at a temperature of 77.279 K using a BET surface analyzer (Micromeritics Instrument Flex Version 6.01).

Scanning electron microscopy (SEM) (Hitachi TM 1000) was used to analyze surface morphology of both PAC and CAC. The elemental analysis of the CAC was performed using SEM–energy dispersive X-ray analysis (Hitachi TM 1000).

The alteration in the CAC's functional group prior to and following adsorption was examined by Fourier-transform infrared (FTIR) spectroscopy (PerkinElmer Spectrum 65) (spectral range: 4,000–400 cm⁻¹). The pH point of zero charge (pH_PZC) was measured using a pH drift approach. In summary, 75 cm² (15 cm × 5 cm) of CAC specimens with 0.25, 0.5, or 0.75 g of adsorbent were each put in 10 flasks holding 80 mL of a 0.1 M NaCl solution but having ranges of pH values from 2 to 11. The pH adjustment was done using 0.1 M HCl and 0.1 M NaOH. The final pH values were measured and compared with the initial pH values after 24 h of equilibrium time. The pH_PZC was calculated by linear interpolation between two neighboring data points whose linear connections intersected with the bisector. The experiment was done in duplicate.

Standard 100 mg/L of Cd (II) and 100 mg/L of Cr (VI) solutions were prepared using the established protocol outlined in the APHA (2005) standard procedure. Different metal ion concentrations were diluted as needed from the stock solutions accordingly for the batch experiments.

Regression analysis and analysis of variance (ANOVA) were performed on the experimental data using Design-Expert version 11 software. This analysis aimed at determining the relationship between the process variables and the experimental response (i.e. simultaneous metal ion removal). The interactions between factors and their effects on the simultaneous removal of Cd (II) and Cr (VI) were elucidated by the utilization of the same software program, which also facilitated the development of response surfaces as three-dimensional curves and two-dimensional contour plots. The model's fit was assessed using the R-square (R²), and its statistical significance and constituent components were evaluated using the F and p-values (95% confidence level), respectively.

Adsorption isotherms were obtained by using a batch reactor in an automated water bath shaker to determine the key factors influencing Cd²⁺ and adsorption by CAC. Five different initial doses of a mixture of Cd²⁺ and (i.e. 10, 7, 5, 5, and 1 mg/l) were utilized to evaluate adsorption at optimized operating conditions, except for the initial adsorbate concentration. In each experiment, 0.25 g of CAC was placed in 100 mL of solute solution of pH 8.5 contained in 150 mL Erlenmeyer flasks. These flasks were then subjected to agitation in a water bath shaker for 105 min at 22.2°C. For quality control and data validity, all experiments were done in triplicate.

Following the determination of metal ion removal (%) and the quantity of metal ions adsorbed on CAC (q in mg g⁻¹) using Equations (3) and (4), respectively, the obtained data were subjected to fitting with Langmuir, Freundlich, Dubinin–Radushkevich, and the Temkin isotherm models to examine the adsorption mechanisms between CAC and the metal ions. Table 2 displays the isotherm model equations (Equations (5)–(8)) and the types of plots employed in determining the parameters of the model for data analysis.

Table 2

Isotherm models and their linear equations

Model .	Isotherm linear form .	Plot .	Equation number .
Langmuir			(5)
Freundlich			(6)
Dubinin–Radushkevich			(7)
Temkin			(8)

Model .	Isotherm linear form .	Plot .	Equation number .
Langmuir			(5)
Freundlich			(6)
Dubinin–Radushkevich			(7)
Temkin			(8)

Notes: K_L is the Langmuir equilibrium constant (l g⁻¹); K_F is the Freundlich equilibrium constant (l g⁻¹); C_e is the metal ion concentration in the solution at equilibrium (mg l⁻¹); q_e denotes the CAC adsorption capacity at equilibrium in solid phase (mg g⁻¹); q_m is related to the monolayer maximum adsorption capacity (mg g⁻¹); 1/n is Freundlich adsorption intensity where ‘n’ is the Freundlich exponent; q_mDR (mg/g) denotes capacity of the saturation theory in the Dubinin–Radushkevich model equation where K_DR (mol²/kJ) is the unit of energy and the Polanyi potential is given by ε. In the Temkin isotherm, B_T is the Temkin constant as it relates to the heat of sorption (J/mol), R is the common gas constant (8,314 J/mol K), T denotes the absolute temperature (K), and K_T denotes the Temkin equilibrium constant corresponding to the highest binding energy (l/g).

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Information on adsorption mechanism and potential rate-controlling actions is provided through kinetic modeling. The adsorption rate influences the selection of materials for use as adsorbents. As a rule of thumb, adsorbent materials should have relatively a high adsorption affinity, a significant adsorption capacity, and a rapid adsorption rate (Nyangi et al. 2020). In this study, the rates of Cd²⁺ and adsorption on the CAC surface were evaluated by adsorption kinetic models: pseudo-first-order, pseudo-second-order, Elovich, and the intraparticle diffusion models shortened as PFO, PSO, EM, and IP, respectively (Wang & Guo 2020). The adsorption kinetic linear equations and the associated plots are presented in Equations (9)–(12) (Table 3).

Table 3

Linear forms and plots of the evaluated adsorption kinetic models

Model .	Isotherm Linear form .	Plot .	Equation number .
PFO			(9)
PSO			(10)
EM			(11)
IP			(12)

Model .	Isotherm Linear form .	Plot .	Equation number .
PFO			(9)
PSO			(10)
EM			(11)
IP			(12)

Notes: For PFO, q_e denotes the equilibrium CAC adsorption capacity (g mg⁻¹), q_t denotes the amount of adsorbate adsorbed time t (g mg⁻¹), while K₁ denotes the PFO rate constant (min⁻¹). For PSO, q_e denotes the equilibrium CAC adsorption capacity (g mg⁻¹), q_t denotes the amount of adsorbate adsorbed time t (g mg⁻¹), whereas K₂ denotes the PSO rate constant (g mg⁻¹ min⁻¹). For the EM, α is the initial adsorption rate constant (mg g⁻¹ min⁻¹), while β is the desorption process constant (g mg⁻¹). For IP, C denotes the film thickness, k_id denotes the IP kinetic rate constant, q_t denotes the adsorption capacity of CAC at time t (g mg⁻¹), and t denotes the treatment time (min).

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The adsorption kinetic study was conducted using an initial metal ion concentration of 5 mg/l at the optimized operating conditions (i.e. adsorbent dosage = 0.25 g; temperature = 22.2 °C; and pH = 8.5). The adsorbate solution of 200 mL was placed in a 250 mL Erlenmeyer flask, then the mixture underwent agitation using a mechanical shaker at 150 rpm for different durations ranging from 30, 60, 90, and 120 min, upon which after a predetermined agitation time, 20 mL of the supernatant was taken from the 250 mL Erlenmeyer flask for ICP-OES analysis. Subsequently, metal ion removal efficiency and the quantification of adsorbed quantities of Cd (II) and Cr (VI) after a given treatment time (t) were calculated using Equations (3) and (4), respectively.

The SEM images of CAC with different adsorbent dosages (Figure 1(e)–1(j)) illustrate the porous, rough surface resulting from the coated adsorbent, consequently increasing the adsorbent capacity as compared with the plain CCF (Figure 1(a) and 1(b)). The homogenous structure observed in the CAC morphology suggests that the APE–DI–adsorbent mixture was well blended during the preparation of the CAC. Conversely, the bright spots in the after-adsorption SEM images indicate the adsorption of Cd²⁺ and onto CAC (Figure 1(k) and 1(l)) depicting the surfaces of particles after adsorption. The S_BET of the prepared CAC was determined to be 10.6 m² g⁻¹. In a study conducted by Azha et al. (2017) focusing on the removal of industrial dye utilizing a bentonite clay layer supported by an APE, the S_BET of the prepared composite was found to be 5.55 m² g⁻¹. Notably, the S_BET found in this study is comparatively higher than the value of 2.085 m²/g reported by Shamsudin & Shahadat (2019) in their synthesis of a CAC material. The relatively low specific surface areas found in all these studies could be attributed to the challenges with regard to preparing and the difficulty in getting a representative sample of the CAC as a powdered specimen for the BET and porosity analyses.

Table S1 presents the experimental findings, which show the actual reduction of Cd²⁺ and under different operating conditions using the prepared CAC with varying dosages. The actual values of the process variables and their variation limits were selected based on data from preliminary studies and various literature sources.

Clearly, the results of Table S1 indicate that Cd²⁺ removal was generally higher than that of at various operating conditions. The observed higher CAC uptake capacity of Cd²⁺ over could be due to several factors emanating from their ionic properties (Table S2) (Obayomi et al. 2020). With reference to the ionic properties, Cd²⁺ is more adsorbed than probably because Cd²⁺ has smaller (4.260 Å) hydrated radii than (4.61 Å), making it more accessible to adsorbent pores. Again Mohan & Singh (2002) assert that metals with higher standard reduction potential (as demonstrated by Cd²⁺ in Table S2) exhibit enhanced ionic interaction with the electron-rich surface of adsorbents. Furthermore, Cd²⁺ has a higher electronegativity value (1.69) than (1.66), making it exhibit a higher affinity for the adsorbent surface (Kadirvelu et al. 2008; Jain et al. 2016).

The models used for the simultaneous removal of Cd²⁺and show significant high F-values of 10.21 and 8.74, respectively, with low p-values (≤0.0001) for both metal ions. This observation suggests that there is a notable influence on the response variable from at least one of the factors included in each model (Brereton 2019). For Cd²⁺, pH (A), adsorbent dosage (B), and contact time (D) were highly significant to the model, all showing low p-values (p < 0.05). In the case of removal, pH (A), initial concentration (C), and contact time (D) were all highly significant (p < 0.05) to the model. Table 4 shows that pH (A) and contact time (D) were significant (p < 0.05) for removal of both metal ions, while temperature was insignificant (p > 0.05) for removal of both metal ions. Meanwhile, several interaction terms were also found to be significant (p < 0.05) in both models including pH and adsorbent dosage (AB), pH and temperature (AE), and adsorbent dosage and contact time (BD). The square terms of adsorbent dosage (B², p < 0.0001) and temperature (E², p = 0.0011) were highly significant to the Cd²⁺removal model, while the squares of pH (A²), contact time (D²), and temperature (E²) were all significant model terms to removal (p < 0.05). The obtained lack of fit F-values of 0.85 for Cd²⁺ and 0.32 for indicated that the lack of fit is not significant in both models.

The models' comparatively high regression coefficients (R²) of 0.895 and 0.879 for Cd²⁺ and removal, respectively, indicate that both models are capable of accurately predicting the response. The models have also demonstrated a signal-to-noise ratio of 14.2 and 13.4, for the removal of Cd²⁺ and ⁠, respectively, indicating their appropriateness and adequacy. There is a good agreement between predicted and adjusted correlated coefficients as the difference between them is ≤0.2. The factors added to modify the models have improved the models because the adjusted R² (0.81 and 0.78) for both metals is more than the predicted R² (0.62 and 0.63) for Cd²⁺ and ⁠, respectively. Hence, the response surface model developed in this research for predicting efficiency of the removal of both Cd²⁺ and from a binary component aqueous media can be considered satisfactory.

The observations from Figure 6(a) and 6(b) reveal distinct trends in the removal efficiency of the metal ions, with Cd (II) removal increasing with pH and Cr (VI) removal decreasing with pH, which is consistent with the CAC's pH_PZC. Vividly, when the pH of the solution is higher than the determined pH_PZC, the negatively charged surface of the adsorbent provides electrostatic interactions that promote the adsorption of positively charged Cd²⁺. Conversely, as the pH of the solution lowers below the pH_PZC, the surface of the adsorbent becomes positively charged, hence promoting the adsorption of the negatively charged (Mandal et al. 2021; Roy & Bharadvaja 2021; Singh et al. 2023).

Figure 6(c) and 6(d) provide insights into the influence of adsorbent dosage on Cd²⁺ and reduction. It is evident that the adsorbent dosage on the CAC has a significant effect on Cd²⁺ reduction (p < 0.0001) but an insignificant effect on reduction (p = 0.1217). These findings are further supported by the results of the ANOVA as presented in Table 4. Generally, high adsorbent dosage is associated with high removal efficiency due to availability of more vacant sites available for adsorption on the adsorbent (Obayomi et al. 2020; Rahaman et al. 2021) as is observed in Figure 6(c) for Cd²⁺. However, an opposite trend is observed for by CAC whereby removal efficiency decreases with increased adsorbent dosage (Figure 6(d)). This could potentially be attributed to the increase in the PZC on CAC as the adsorbent dosage on the CAC increases, as observed and explained in Figure 3. Conversely the observed decrease in removal efficiency for Cd²⁺ observed after 0.6 g of adsorbent on CAC could be explained in terms of CAC preparation as the total surface area and active sites decrease with increasing adsorbent dosage due to overlapping adsorbents on the CAC surface resulting in decreased adsorbent capacity (Kayranli 2022).

The initial metal ion concentration has an insignificant (p = 0.8881) effect on the Cd²⁺ reduction but significant (<0.0001) effect on reduction by CAC (Figure 6(e) and 6(f)). The decline in the % removal of can be attributed to the finite number of active sites present on the adsorbent, resulting in saturation at concentrations above a particular threshold (Werkneh et al. 2014). At lower initial concentrations, the ratio between the initial quantity of and the accessible active sites of the adsorbent is lower resulting in higher removal efficiency of ⁠. Conversely, at higher concentrations, a greater number of residual ions persist in the aqueous solution (Emirie 2015). Hence, the reduction of is significantly higher at low initial concentrations as there are fewer initial chromium ions relative to the number of active sites that are accessible on the adsorbent. This also explains why more residual Cr (VI) ions are left in the aqueous solution at higher initial concentrations (Gorzin & Abadi 2018).

The influence of contact time was found to be significant for the removal of both metal ions, but higher level of significance was observed for Cd²⁺ (p < 0.0001) than for (p = 0.0230) as indicated in Table 4. In a binary component system, Cd²⁺ removal efficiency by CAC exhibited an increase with increasing contact time as shown in Figure 6(g). Conversely, removal efficiency demonstrated a slight decrease with contact time, attaining its maximum removal efficiency within the first 15 min (Figure 6(h)). The observed pattern of increased Cd²⁺ and decreased removal efficiencies with increased contact time could be attributed to the fact that a longer contact time allows for more dissociation of the base, APE (pH = 0.8) (Jayaram & Prasad 2009; Smith 2011), used in the preparation of CAC. With prolonged contact time, the base dissolves in water to produce hydroxide (OH⁻) ion (American Chemical Society 2019), which in turn favors the adsorption of Cd²⁺ over as observed in Figure 6(g) and 6(h). Independently, temperature was found not to have a significant effect on the removal of both metal ions (Figure 6(i) and 6(j)) in a binary component system.

The primary aim of the optimization process was to ascertain the optimal values of the variables for the removal of Cd²⁺ and using CAC. This was achieved by utilizing a model derived from experimental data. The selection of the operational parameters was made with the objective of optimizing the response, i.e. both Cd²⁺ and removal efficiencies, while initial pH of the sample, adsorbent dosage, initial metal ion concentration, contact time, and temperature were all left at a range. Multiple sets of experiments (100 in total) were suggested by the model, but the one with high desirability score (0.808) was selected for verification and further adsorption experiments, i.e. kinetics, isotherms, and thermodynamics studies (Table S3). The optimized operation conditions (i.e. pH = 8.5, adsorbent dosage = 0.25 g; initial concentration = 5 mg/l, contact time = 105 min, and temperature = 23.73 °C) predicted 86.86 and 94.26% removal for Cd²⁺ and ⁠, respectively. These optimum operating conditions yielded experimental removal efficiencies of 83.98 and 58.08% for Cd²⁺ and ⁠, respectively, corresponding to adsorption capacities of 2.52 mg/g for Cd²⁺ and 1.74 mg/g for ⁠.

Adsorption equilibrium isotherms play a pivotal role in the design of adsorption systems due to their ability to elucidate the interaction mechanisms between pollutants and adsorbent materials. Moreover, they facilitate the prediction of adsorption parameters and enable quantitative comparisons of adsorbent behavior under different experimental conditions (Al-Ghouti & Da'ana 2020). As already outlined in the methodology section, this study employed the Langmuir, Freundlich, Dubinin–Radushkevich, and Temkin isotherm models to fit the experimental data. The derived parameter values are presented in Table 5.

Table 5

Calculated parameters of the adsorption isotherm models using linear regression analysis

Langmuir model
Metal ion	qm (mg/g)	KL (L/mg)	R2 (–)
Cd2+	10.718	0.1367	0.9986
	24.231	0.0549	0.9861
Freundlich model
Metal ion	1/n (–)	KF ((mg/g)/(L/mg)n)	R2 (–)
Cd2+	0.896	1.754	0.9933
	1.278	1.266	0.9644
Dubinin–Radushkevich
Metal ion	qDR (mg/g)	βDR (×10−8 mol2/kJ2)	R2 (–)
Cd2+	4.167	−1.6 × 10−4	0.8787
	4.296	−4.6 × 10−5	0.8379
Temkin
Metal ion	KT (L mol−1)	bT (mg/g)	R2 (–)
Cd2+	0.127185	106.3501	0.8361
	0.173303	84.43782	0.8288

Langmuir model
Metal ion	qm (mg/g)	KL (L/mg)	R2 (–)
Cd2+	10.718	0.1367	0.9986
	24.231	0.0549	0.9861
Freundlich model
Metal ion	1/n (–)	KF ((mg/g)/(L/mg)n)	R2 (–)
Cd2+	0.896	1.754	0.9933
	1.278	1.266	0.9644
Dubinin–Radushkevich
Metal ion	qDR (mg/g)	βDR (×10−8 mol2/kJ2)	R2 (–)
Cd2+	4.167	−1.6 × 10−4	0.8787
	4.296	−4.6 × 10−5	0.8379
Temkin
Metal ion	KT (L mol−1)	bT (mg/g)	R2 (–)
Cd2+	0.127185	106.3501	0.8361
	0.173303	84.43782	0.8288

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Table 6 presents the parameters of the PFO, PSO, EM, and IP as well as the findings of their validation.

By conforming to the PSO model, it indicates that the CAC has abundance of active sites for the adsorption of both Cd (II) and Cr (VI) (Wang & Guo 2020). The rates of Cd²⁺ and adsorption onto CAC in the present study were determined to be 0.056 and 0.337 g/mg/min, respectively.

The thermodynamic process parameters of Cd²⁺ and adsorption on CAC, i.e. enthalpy (ΔH°), entropy (ΔS°), and Gibbs free energy (ΔG°), were estimated using Equations (13)–(15) respectively. The adsorption experiments involved thermodynamic considerations to determine the spontaneity and feasibility of such processes. The thermodynamic parameter results are summarized in Table 7.

Table 7

CAC thermodynamic parameters for Cd²⁺ and removal from aqueous solution

Metal ion .	T (K) .	ΔG° (KJ mol−1) .	ΔH° (KJ mol−1) .	ΔS° (KJ mol−1) .	Intercept .	Slope .
	296.15	−6.69844	1.49203	27.6507	3.3258	− 179.46033
	308.15	−7.01488
	318.15	−7.32742
	328.15	−7.57085
Cd (II)	296.15	−6.92246	5.96698	44.1149	5.3061	− 717.7025
	308.15	−8.03445
	318.15	−7.84663
	328.15	−8.49812

Metal ion .	T (K) .	ΔG° (KJ mol−1) .	ΔH° (KJ mol−1) .	ΔS° (KJ mol−1) .	Intercept .	Slope .
	296.15	−6.69844	1.49203	27.6507	3.3258	− 179.46033
	308.15	−7.01488
	318.15	−7.32742
	328.15	−7.57085
Cd (II)	296.15	−6.92246	5.96698	44.1149	5.3061	− 717.7025
	308.15	−8.03445
	318.15	−7.84663
	328.15	−8.49812

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The negative ΔG° and positive ΔH° values in Table 7 indicate that the adsorption process is spontaneous and endothermic, respectively. While there are no specific criteria pertaining to the ΔH° values defining the adsorption, it is commonly assumed that heats of adsorption ranging from 20.9 to 418.4 kJ/mol, which are indicative of chemical reactions, are comparable to the heats associated with the chemisorption processes (Azha et al. 2014; Gorzin & Abadi 2018). Conversely, low enthalpy of adsorption (5–0 kJ/mol) is associated with physisorption (Subhabrata & Gargi 2020). Hence, based on the results of ΔH° values obtained for both pollutants, the study's findings suggest that the adsorption of Cd²⁺ and onto CAC proceeded via physisorption. The SEM images presented and observed in Figure 1(k) and Figure 1(l) also conform to this finding.

Entropy (ΔS°) is defined as a measure of randomness or disorder of a system whereby when the disorder of a system decreases, it causes negative entropy (Al-Ghouti & Da'ana 2020). Inversely, a positive shift in ΔS° is indicative of the adsorbent's affinity for the adsorbate as well as an indication of increasing randomness at the solid and liquid interface. This may be accompanied by structural modifications in both the adsorbent and adsorbate (Ebelegi et al. 2020). The current investigation demonstrates that the ΔS° values for both heavy metals suggest an increase in randomness at the interface between the adsorbent and solution. This increase in randomness serves as the primary driving mechanism for the adsorption of both metal ions onto CAC.

The study focused on the development of CAC made from PJAC, aiming to efficiently remove metal ions from water without a need for traditional separation methods such as filtration or centrifugation. The prepared adsorbent composite was characterized by means of S_BET, FTIR, SEM (morphology), and pH_PZC. An experimental design utilizing the BBD of the RSM was employed to assess the effects of process variables and their interactions on simultaneous removal of cation and anion metals.

The results revealed that pH and contact time were the most significant (p < 0.05) factors on the removal of both Cd²⁺ and ⁠, underscoring their critical roles in the adsorption process. In addition, adsorbent dosage was the most significant factor (p < 0.0001) for Cd²⁺ reduction, while initial concentration had a highly significant impact on reduction (p < 0.0001). It was also found that the interaction effects between pH and adsorbent dosage, pH and temperature, as well as adsorbent dosage and contact time contributed significantly to the percent reduction of both metal ions (p < 0.05). The optimized operation conditions (pH = 8.5, adsorbent dosage = 0.25 g, initial concentration = 5 mg/l, contact time = 105 min, and temperature = 23.73 °C) led to the substantial removal efficiencies of 86.86 and 94.26% for Cd²⁺ and ⁠, respectively. Moreover, experimentally the optimized operation parameters for CAC successfully reduced the concentration of both metals by 83.98% for Cd²⁺ and 58.08% for ⁠. The Langmuir isotherm model proved to be the most suitable for explaining the adsorption of both metal ions by the CAC in a binary component system. Furthermore, the kinetic behavior of the removal of both metal ions was accurately described by the PSO kinetic model. Thermodynamic analysis revealed that the adsorption of Cd (II) and Cr (IV) ions onto the adsorbent was endothermic, with positive ΔS° values indicating the increased randomness at the adsorbent–solution interface. Conversely, negative ΔG° values demonstrated the spontaneity of the adsorption process.

Overall, the study's findings have underscored the undeniable potential of CAC as an effective adsorbent for the reduction of heavy metals from industrial effluent. Furthermore, the CAC's ability to eliminate the requirement for filtration, sedimentation, or centrifugation steps makes it a compelling candidate for scalable and practical industrial applications.

The authors express their sincere gratitude to the staff at both the Department of Separation Science at LUT University, Finland, and the Institute of Catalysis and Petrochemistry (ICP) of the Spanish National Research Council (CSIC), Spain, for their invaluable support in conducting SEM analysis and isotherm experiments for estimating BET surface area, respectively. The funding for this research was provided by the Africa Centre of Excellence for Water Management (ACEWM) at Addis Ababa University.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.