To overcome the challenges associated with powdered activated carbon (PAC) in water and wastewater treatment, the efficacy of composite adsorbent coating (CAC) synthesized using a simple sol-gel method with Prosopis juliflora-activated carbon for the simultaneous reduction of Cd2+ and Cr2O72− was investigated. The CAC was characterized by FTIR (C-H, C = O, and O-H stretching), pHPZC (6 -6.6), SEM (porous-rough surface), and BET surface area (10.6 m2/g) techniques. Statistical analysis confirmed that pH and contact time significantly (p < 0.0001) affected both metal ions removal, with Cd2+ removal generally exceeding that of Cr2O72− due to better ionic properties. Using the optimized conditions (8.5 pH, 0.25 dosage, 5 mg concentration, 105 minutes contact time and 23.73 °C temperature), the predicted and experimental ion removal efficiencies were 86.86 and 83.98% for Cd2+ and 94.26 and 58.08% for Cr2O72−, respectively. The Langmuir adsorption isotherm was the best-suited model (R2 > 0.99), while the metal ions removal was regulated by the PSO kinetic model (R2 > 0.999). The adsorption process was endothermic and spontaneous, as indicated by thermodynamic values (−ΔG°, +ΔH°, +ΔS°). The study demonstrates CAC's effectiveness as an alternative to PAC, offering significant advantages in removing metal ions from wastewater.

  • Invasive Prosopis juliflora was utilized for composite adsorbent synthesis.

  • Composite was synthesized using a novel sol-gel method.

  • Composite adsorbent was highly stable and non-leaching.

  • Easy adsorbate–adsorbent separation was achieved after treatment.

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.

Chemicals and materials

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).

Preparation of Prosopis juliflora–activated carbon

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.

Preparation of the CAC

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.

Analysis of physicochemical properties

A CAC-powdered sample was prepared for both the Brunauer–Emmet–Teller (BET) surface area (SBET) 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 SBET 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 (pHPZC) 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 pHPZC was calculated by linear interpolation between two neighboring data points whose linear connections intersected with the bisector. The experiment was done in duplicate.

Design of experiments

The response surface methodology (RSM)'s Box–Behnken Design (BBD) was employed in the experimental design. Kaur et al. (2021) define RSM as a set of mathematical and statistical techniques employed for experimental design, process modeling, analysis of independent process variables and their interactions, and determination of optimal parameter values to achieve desired outcomes. The influence of initial concentrations of metal ions, adsorbent dosage, pH, temperature, and agitation time on Cd (II) and Cr (VI) adsorption into the CAC in a binary component system was studied by employing the BBD of the RSM. Equation (1) was used to code the independent variables:
formula
(1)
where Xi denotes the dimensionless coded value of the of ith variable, X0 stands for the central variable value, and ΔX indicates the change in step. The parameters were coded with three (3) levels: +1, 0, and −1, representing the high, center, and low levels, respectively. The independent parameter levels were based on values obtained from various studies as well as prior pilot experiments. The experimental design methodology for each independent variable is presented in Table 1.
Table 1

Levels of independent variables and experimental range for Cd (II) and Cr (VI) adsorption experiments

FactorVariablesLevels of the coded variables
− 10+ 1
pH 
Metal ion concentration (initial) (mg/l) 27.5 50 
Temperature 20 30 40 
Adsorbent dosage (g) 0.25 0.5 0.75 
Contact time (min) 15 60 105 
FactorVariablesLevels of the coded variables
− 10+ 1
pH 
Metal ion concentration (initial) (mg/l) 27.5 50 
Temperature 20 30 40 
Adsorbent dosage (g) 0.25 0.5 0.75 
Contact time (min) 15 60 105 
The empirical second-order polynomial model (Khedmati et al. 2017) was used for fitting the experimental data. The model terms are represented by Equation (2):
formula
(2)
where Y corresponds to the variable response (metal ion removal). The coefficients b0 to bij are constant values where b0 is the intercept, bi is the linear term, bii is the quadratic term, and bij is the interactive term. Xi to Xj are the independent variables presented in Table 1. A series of 45 experiments including five replications at the central points were conducted in a batch adsorption setup for Cd (II) and Cr (VI) simultaneous removal.

Heavy metal adsorption experiments and removal efficiency

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.

For each of the 45 batch adsorption experiments, 100 mL of the adsorbate was carefully transferred into an Erlenmeyer flask (150 mL) and the required CAC dosage was added/put into the Erlenmeyer flask with pH adjustment made as needed. All the mixtures were simultaneously shaken in an automated water bath shaker at 150 revolutions per minute (rpm) while varying the temperature and the contact time accordingly as presented in Table 1. A pH meter (Eutech instruments PH 700 series) was used to measure and adjust the adsorbate's pH before adsorption experiments. Inductively coupled plasma–optical emission spectrometry (ICP-OES) (Agilent 5800) was employed to quantify the concentration of the adsorbate before and after the adsorption experiment. Equation (3) was used in calculating the metal ion removal efficiency (%), while Equation (4) was employed in determining the CAC adsorption capacity (mg/g):
formula
(3)
formula
(4)
where E denotes the metal ions percent reduction efficiency (%), Ci is the initial metal ion concentration (mg l−1), and Ct denotes the final metal concentration (mg l−1), denotes the adsorbent metal ion adsorption capacity (mg/g), v denotes the adsorbate volume (l), and the m denotes the mass of the adsorbent (g).

Model fitting and statistical analysis

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 isotherm experiments

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

ModelIsotherm linear formPlotEquation number
Langmuir   (5) 
Freundlich   (6) 
Dubinin–Radushkevich 
 
 (7) 
Temkin   (8) 
ModelIsotherm linear formPlotEquation number
Langmuir   (5) 
Freundlich   (6) 
Dubinin–Radushkevich 
 
 (7) 
Temkin   (8) 

Notes: KL is the Langmuir equilibrium constant (l g−1); KF is the Freundlich equilibrium constant (l g−1); Ce is the metal ion concentration in the solution at equilibrium (mg l−1); qe denotes the CAC adsorption capacity at equilibrium in solid phase (mg g−1); qm is related to the monolayer maximum adsorption capacity (mg g−1); 1/n is Freundlich adsorption intensity where ‘n’ is the Freundlich exponent; qmDR (mg/g) denotes capacity of the saturation theory in the Dubinin–Radushkevich model equation where KDR (mol2/kJ) is the unit of energy and the Polanyi potential is given by ε. In the Temkin isotherm, BT 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 KT denotes the Temkin equilibrium constant corresponding to the highest binding energy (l/g).

Adsorption kinetic experiments

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

ModelIsotherm Linear formPlotEquation number
PFO   (9) 
PSO   (10) 
EM   (11) 
IP   (12) 
ModelIsotherm Linear formPlotEquation number
PFO   (9) 
PSO   (10) 
EM   (11) 
IP   (12) 

Notes: For PFO, qe denotes the equilibrium CAC adsorption capacity (g mg−1), qt denotes the amount of adsorbate adsorbed time t (g mg−1), while K1 denotes the PFO rate constant (min−1). For PSO, qe denotes the equilibrium CAC adsorption capacity (g mg−1), qt 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, kid denotes the IP kinetic rate constant, qt denotes the adsorption capacity of CAC at time t (g mg−1), and t denotes the treatment time (min).

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.

Thermodynamic experiments

The study also examined the effect of temperature and thermodynamic variables on Cd2+ and adsorption onto the CAC. Thermodynamic experiments were conducted by adding 0.1 l solution of 5 mg/l of both Cd2+ and into Erlenmeyer flasks (150 mL). In addition, a 0.25 g dosage CAC was placed in each flask. While maintaining an agitation speed of 150 rpm for a duration of 105 min; the flasks temperatures were subsequently adjusted to 288.15, 303.15, and 318.13 K by adjusting the temperature of the automated water bath shaker. The thermodynamic parameters measured are ΔG°, the Gibbs free energy in J/mol; ΔS°, the entropy in kJ/mol; and ΔH°, the enthalpy variations in kJ/mol. Equations (13)–(15) were utilized to calculate the values of ΔG°, ΔH°, and ΔS°, respectively (Kwikima et al. 2022):
formula
(13)
formula
(14)
formula
(15)
where ΔG° denotes the standard free energy change in J/mol, ΔH° denotes the standard enthalpy change in kJ/mol, ΔS° denotes the standard entropy change in J/mol K, T denotes the solution absolute temperature (K), R denotes the universal gas constant (8.314 J/mol K), Kc denotes the equilibrium constant, qe denotes the equilibrium adsorption capacity (mg/g), and Ce denotes the equilibrium concentration of metal ions in mg/l. Both ΔS° and ΔH° were computed from ln Kc vs. 1/T plot intercept and slope values.

CAC characterization

SEM and BET analysis

The SEM images of plain CCF, PAC, and CAC (before and after adsorption of Cd2+ and ) were captured at different magnifications as shown in Figure 1. In the SEM micrograph of the plain CCF (Figure 1(a) and 1(b)), the cotton fibers of the CCF are very evident but lack a rough structure. Clearly, the cellulosic fibers are arranged in a loosely spiral network in the plain CCF sample. The presence of intrafibrous holes in the CCF clearly contributes to its high surface area. According to Kumar et al. (2022), cotton fiber is predominantly composed of cellulose, accompanied by a limited number of non-cellulosic components that encompass the cellulose core. CCF's large surface area, low weight, ease of handling, and affordable pricing (Azha et al. 2017) make it an ideal substrate. These characteristics make the use of CCF in wastewater treatment facilities practical and relatively cost-effective. Figure 1(c) and 1(d) present the SEM image of the PAC used for making the CAC. The PAC surface morphology was notably rough and porous.
Figure 1

SEM images (with different magnifications) of plain uncoated CCF (a and b), PAC (c and d), 0.25 g CAC (e and f), 0.5 g CAC (g and h), 0.75 g CAC (i and j) before adsorption and contaminated 0.25 CAC after adsorption of Cd2+ and (k and l).

Figure 1

SEM images (with different magnifications) of plain uncoated CCF (a and b), PAC (c and d), 0.25 g CAC (e and f), 0.5 g CAC (g and h), 0.75 g CAC (i and j) before adsorption and contaminated 0.25 CAC after adsorption of Cd2+ and (k and l).

Close modal

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 SBET 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 SBET of the prepared composite was found to be 5.55 m2 g−1. Notably, the SBET 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.

FTIR analysis

The FTIR analysis was performed to ascertain the presence of functional groups on the surface of the CAC. In addition, FTIR was utilized to evaluate any alterations in the functional groups of the clean and loaded CAC. The FTIR spectra of APE and the prepared CAC are presented in Figure 2(a)–2(e). The broad APE peak at 3,000–2,830 cm−1 and the sharp APE spectra peak at 1,780–1,670 cm−1 correspond to C–H and C = O group stretching. The appearance of both peaks confirms the presence of a carboxylic acid rather than an alcohol (Smith 2011). Conversely, the broad peak observed at 3,700–3,200 cm−1 in the CAC corresponds to O–H stretching. Vividly the intensity of this peak increased as the adsorbent dose on CAC also increased. After the adsorption of Cd2+ and , the CAC shows an almost complete disappearance of the peak heights of the FTIR spectrum (Figure 2(e)). The observed reduction in peak intensity in the FTIR spectra following Cd2+ and adsorption shows that the functional groups are fully involved in the adsorption process. Moreover, the disappearance of the peaks reveals that there was a strong characteristic adsorption of either Cd2+ or or both onto the surface of the CAC. The decrease in the intensities of the functional groups on the CAC could be a result of the bonding of the metal ions with functional groups via the oxidation–reduction process (Singh et al. 2023).
Figure 2

FTIR spectra of APE (a) 0.75 g CAC, (b) 0.5 g CAC, (c) 0.25 g CAC before (d), and 0.25g CAC after adsorption of Cd2+ and (e).

Figure 2

FTIR spectra of APE (a) 0.75 g CAC, (b) 0.5 g CAC, (c) 0.25 g CAC before (d), and 0.25g CAC after adsorption of Cd2+ and (e).

Close modal

pHPZC determination

The surface property of the CAC in this study was determined by the pHPZC which represents the pH value at which the surface of the adsorbent attains overall electrical neutrality (Deng et al. 2009). As shown in Figure 3, the pHPZC of the prepared CAC is slightly acidic but increases with adsorbent dosage. It is shown that the 0.25 g CAC exhibited a pHPZC of 6, while the 0.5 and 0.75 g CAC both had a pHPZC of 6.6. When comparing the pHPZC values among the CACs of different dosages, the relatively lower pHPZC observed on the 0.25 g CAC (6.0) in comparison with the other CAC dosages (6.66) could be attributed to the basic nature of the APE that served as the binder of the adsorbent onto the substrate (MCP 2012). Nevertheless, it was crucial to understand the adsorbent's PZC because it affects the electrostatic interactions occurring between the adsorbate and CAC. For instance, negatively charged adsorbent surfaces foster electrostatic interactions that facilitate the adsorption of positively charged ions, such as Cd2+, when the solution pH is higher than pHPZC. However, as the pH of the solution drops, the positively charged adsorbent surface facilitates the adsorption of negatively charged ions like (Renu et al. 2023).
Figure 3

pHPZC of the prepared CAC with different adsorbent dosages.

Figure 3

pHPZC of the prepared CAC with different adsorbent dosages.

Close modal

Cd2+and simultaneous removal

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).

Evaluation of the batch adsorption data

Regression model development and validation

Operating parameters like pH, adsorbent dosage, contact time, initial concentration, temperature, and their interactions affect the efficiency of removing contaminants from aqueous solutions (Amrutha et al. 2023). Accordingly, batch adsorption experiments were performed to study the combined effect of the operating factors on simultaneous removal of Cd2+ and from aqueous media using CAC. Table S1 summarizes the results of the experiments on different combinations of operating parameters. Equation (2) illustrates the response function's coefficients and Table 4 presents the p-values and F-values for assessing the validity of the model prediction for simultaneous removal efficiencies of Cd2+ and . The actual second-order polynomial equations describing the CAC Cd2+ and removal efficiencies are given in Equations (16) and (17), respectively:
formula
(16)
formula
(17)
where T is the contact time, C is the initial concentration, AD is the adsorbent dosage, and Temp is the temperature.
Table 4

Regression analysis and ANOVA results for quadratic model response surface for Cd2+and removal by CAC

SourceCd (II) response
Cr (VI) response
F-valuep-valueF-valuep-value
Model 10.21 <0.0001 8.74 <0.0001 Significant 
A: pH 19.72 0.0002 10.07 0.0041  
B: adsorbent dosage 52.82 <0.0001 2.57 0.1217  
C: initial concentration 0.0232 0.8801 33.33 <0.0001  
D: contact time 35.83 <0.0001 5.90 0.0230  
E: temperature 3.39 0.0782 0.0185 0.8930  
AB 25.98 <0.0001 13.72 0.0011  
AC 0.1265 0.7252 3.91 0.0595  
AD 0.0054 0.9421 7.36 0.0121  
AE 9.97 0.0043 11.37 0.0025  
BC 0.8998 0.352 29.25 <0.0001  
BD 5.19 0.0318 5.72 0.0249  
BE 3.07 0.0926 2.448 0.1281  
CD 4.18 0.0519 10.73 0.0032  
CE 0.4648 0.5019 0.2901 0.5951  
DE 0.0401 0.8430 0.6753 0.4193  
Lack of fit 0.8508 0.6485 0.3165 0.9636 Not significant 
R2 0.8948   0.8793     
Adjusted R2 0.8072 0.7787 
Predicted R2 0.6281 0.6311 
Adequate precision 14.2322 13.4231 
SourceCd (II) response
Cr (VI) response
F-valuep-valueF-valuep-value
Model 10.21 <0.0001 8.74 <0.0001 Significant 
A: pH 19.72 0.0002 10.07 0.0041  
B: adsorbent dosage 52.82 <0.0001 2.57 0.1217  
C: initial concentration 0.0232 0.8801 33.33 <0.0001  
D: contact time 35.83 <0.0001 5.90 0.0230  
E: temperature 3.39 0.0782 0.0185 0.8930  
AB 25.98 <0.0001 13.72 0.0011  
AC 0.1265 0.7252 3.91 0.0595  
AD 0.0054 0.9421 7.36 0.0121  
AE 9.97 0.0043 11.37 0.0025  
BC 0.8998 0.352 29.25 <0.0001  
BD 5.19 0.0318 5.72 0.0249  
BE 3.07 0.0926 2.448 0.1281  
CD 4.18 0.0519 10.73 0.0032  
CE 0.4648 0.5019 0.2901 0.5951  
DE 0.0401 0.8430 0.6753 0.4193  
Lack of fit 0.8508 0.6485 0.3165 0.9636 Not significant 
R2 0.8948   0.8793     
Adjusted R2 0.8072 0.7787 
Predicted R2 0.6281 0.6311 
Adequate precision 14.2322 13.4231 

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.

Figure 4 shows the normal percentage probability of residual against the normal plot of residuals for Cd2+ (Figure 4(a)) and (Figure 4(b)) removal, respectively. Both plots display an S-shaped pattern and form a fairly straight line, demonstrating a good correlation between probability and normal reduction, which confirms the assumption that both models are suitable for predicting the removal efficiencies of both metals.
Figure 4

Normal probability plots for CAC on (a) cadmium removal and (b) chromium removal.

Figure 4

Normal probability plots for CAC on (a) cadmium removal and (b) chromium removal.

Close modal
Figure 5 shows the predicted and actual plots confirming the correspondence between the residual and predicted simultaneous Cd2+ (Figure 5(a)) and (Figure 5(b)) reduction implying that the models could well predict within the range of operating parameters. Consequently, one can assert that the quadratic models of the response surface developed in this study that relate the reduction of Cd2+ and with operating parameters are most suitable for describing the experimental outcomes of the CAC adsorption process in a binary component system.
Figure 5

Plots for predicted against actual results for (a) Cd (II) and (b) Cr (VI) reduction by CAC.

Figure 5

Plots for predicted against actual results for (a) Cd (II) and (b) Cr (VI) reduction by CAC.

Close modal

Effect of operating parameters on simultaneous removal of Cd2+ and

Various studies have emphasized that the important operation parameters affecting the performance of adsorption process are initial pH of solution, initial concentration of pollutant, contact time, solution temperature, and adsorbent dosage (Sreedhar & Reddy 2019; Mandal et al. 2021; Qasem et al. 2021; Roy & Bharadvaja 2021). In the present study, the BBD was used for the optimization of the aforementioned parameters. The effects of these parameters on Cd2+ and adsorption onto CAC were shown by the response surface plots, while the rest of the operating conditions were set at their mid points (Figure 6).
Figure 6

Main plots of operating parameters affecting Cd2+ and removal efficiency by CAC.

Figure 6

Main plots of operating parameters affecting Cd2+ and removal efficiency by CAC.

Close modal

Effect of pH

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 pHPZC. Vividly, when the pH of the solution is higher than the determined pHPZC, 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 pHPZC, 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).

Effect of adsorbent dosage

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).

Effect of initial metal ion concentration

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).

Effect of contact time and temperature

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.

Combined influence of operating conditions on simultaneous Cd2+ and removal efficiency

In this investigation, among the total of 10 potential combinations between any two operational parameters affecting Cd2+ and removal efficiency established by RSM, only three combinations had significant (p ≤ 0.05) effects on the removal of both metal ions, i.e. pH and adsorbent dosage (AB), pH and temperature (AE), and adsorbent dosage and contact time (BD) as already presented in Table 4. The interaction of the most significant factors for both metal ions has been shown for any two parameters simultaneously (Figure 7(a)–(f)).
Figure 7

Response surface of experimental data showing interacting effect of most significant operating factors of CAC on Cd2+ and Cr2O72− reduction (adsorbent dosage and pH (a-b), temperature and pH (c-d) and contact time and adsorbent dosage (e-f)).

Figure 7

Response surface of experimental data showing interacting effect of most significant operating factors of CAC on Cd2+ and Cr2O72− reduction (adsorbent dosage and pH (a-b), temperature and pH (c-d) and contact time and adsorbent dosage (e-f)).

Close modal

Process optimization

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 isotherm study

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 
DubininRadushkevich 
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 
DubininRadushkevich 
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 

According to the data provided in Table 6, it can be noticed that the Langmuir model had the highest correlation coefficient (R2) values for both metal ions, specifically Cd2+ (R2 = 0.998) and (R2 = 0.986). Similarly, Figure 8 shows that the Langmuir model is the best-suited adsorption isotherm model for the removal of both metal ions by the CAC in a binary component system. Based on the aforementioned observation, it can be concluded that the sorption of both metal ions onto CAC was on a monolayer and homogenous surface (Kumar & Chauhan 2019; Al-Ghouti & Da'ana 2020). This is because the underlying assumption of the model is that the adsorbent sites exhibit uniformity and homogeneity, with equal amounts of energy, and that the sites have an equal chance of adsorbing pollutants (Al-Ghouti & Da'ana 2020). The present finding is consistent with the research conducted by Azha et al. (2014), where they also developed an adsorbent coating layer called Paintosorp that was coated on a glass surface. The adsorption capabilities of this coating were then evaluated on the removal of methylene blue through a batch adsorption setup.
Table 6

Calculated parameters of the adsorption kinetics models of the binary component system

PFO model 
Metal ion K1 (min−1) R2 (–) qe(mg/g) (exp) qe(mg/g) (calc) 
Cd2+ −0.0004 0.92301 3.5677 0.1082 
 −0.0006 0.5850 3.2662 1.6910 
PSO model 
Metal ion K2 (g mg−1 min−1) R2 (–) qe(mg/g) (exp) qe(mg/g) (calc) 
Cd2+ 0.0555 0.9994 3.5677 3.6350 
 0.3368 0.9999 3.2662 3.2862 
EM 
Metal ion α(mg g−1 min−1 β(g/mg) R2 (–)  
Cd2+ 4.0209 2721.27 0.9665  
 23.1107 1.9988E + 29 0.9686  
IP model 
Metal ion KP(mg/g h1/2) C(mg/g) R2 (–)  
Cd2+ 0.06285 2.82759 0.94043  
 0.01106 3.14362 0.97618  
PFO model 
Metal ion K1 (min−1) R2 (–) qe(mg/g) (exp) qe(mg/g) (calc) 
Cd2+ −0.0004 0.92301 3.5677 0.1082 
 −0.0006 0.5850 3.2662 1.6910 
PSO model 
Metal ion K2 (g mg−1 min−1) R2 (–) qe(mg/g) (exp) qe(mg/g) (calc) 
Cd2+ 0.0555 0.9994 3.5677 3.6350 
 0.3368 0.9999 3.2662 3.2862 
EM 
Metal ion α(mg g−1 min−1 β(g/mg) R2 (–)  
Cd2+ 4.0209 2721.27 0.9665  
 23.1107 1.9988E + 29 0.9686  
IP model 
Metal ion KP(mg/g h1/2) C(mg/g) R2 (–)  
Cd2+ 0.06285 2.82759 0.94043  
 0.01106 3.14362 0.97618  
Figure 8

The best fit isotherm model for both (a) Cd2+ and (b) adsorption onto CAC in a binary component system.

Figure 8

The best fit isotherm model for both (a) Cd2+ and (b) adsorption onto CAC in a binary component system.

Close modal

The kinetics of Cd (II) and Cr (VI) adsorption onto CAC

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

Of the four kinetic models tested, the PSO kinetic model showed the highest correlation values for Cd2+ (R2 = 0.9994) and (R2 = 0.9999) as illustrated in Figure 9. This confirms that the kinetics of the removal of both metal ions by CAC was regulated by PSO.
Figure 9

The best fit kinetic model for both Cd2+ and adsorption onto CAC in a binary component system.

Figure 9

The best fit kinetic model for both Cd2+ and adsorption onto CAC in a binary component system.

Close modal

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.

Determination of adsorption thermodynamics parameters

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 ionT (K)ΔG° (KJ mol−1)ΔH° (KJ mol−1)ΔS° (KJ mol−1)InterceptSlope
 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 ionT (K)ΔG° (KJ mol−1)ΔH° (KJ mol−1)ΔS° (KJ mol−1)InterceptSlope
 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 

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 Δ 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 Δ 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 SBET, FTIR, SEM (morphology), and pHPZC. 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.

1

mz:mp, where mz is the mass of ZnCl2 and mp is the mass of dried Prosopis juliflora.

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

The authors declare there is no conflict.

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