Preparation, characterization and application of polymeric ultra-permeable biodegradable ferromagnetic nanocomposite adsorbent for removal of Cr(VI) from synthetic wastewater: kinetics, isotherms and thermodynamics

Hexavalent chromium (Cr(VI)) contamination in drinking water due to industrial activities is a growing worldwide concern. Cr(VI) concentrations exceeding a few parts per billion (ppb) can cause serious health problems such as asthma, blood cancer, kidney-related diseases, liver and spleen damage, as well as neurological system, immunological deficiencies, and reproductive issues. This study, thus, explored the feasibility of employing a novel polymeric ferromagnetic nanocomposite adsorbent made of low-cost, biodegradable, and ultra-permeable materials from pulp and paper sludge for adsorptive removal of hexavalent chromium (Cr⁶⁺) from synthetic wastewater. Vibrating-sample magnetometer (VSM), X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer-Emmet-Teller surface area (BET), and Fourier transform infrared (FTIR) were used to analyze the produced nanocomposite adsorbent. The Fourier transform infrared results confirmed the presence of adsorptive peaks attributed to −OH, −NH2, and FeO. Scanning electron microscopy micrographs revealed a porous adsorbent surface. XRD revealed the existence of the crystalline spinel-structured magnetite (Fe₃O₄) phase of iron oxide, while the saturation magnetization was established to be 26.90 emu/g. The Brunauer–Emmett–Teller analysis confirmed a slight decrease in the surface area of the nanocomposite adsorbent to 6.693 m².g⁻¹, compared to Fe₃O₄ (7.591 m².g⁻¹). The optimum conditions for Cr⁶⁺ removal were pH 2.0, 1.0 g/L adsorbent dose, room temperature (25°C), 120 min contact time, and 20 mg/L pollutant concentration. During removal, the Cr(VI) was adsorbed by electrostatic attraction and/or reduced to trivalent chromium Cr(III). At low starting Cr(VI) concentrations, chemisorption dominated the removal process, but as concentrations increased, physisorption became more significant. The prepared nanocomposite adsorbent presented exceptional removal efficiency of up to 92.23%, indicating that it may be useful for the adsorption of metal ions from industrial and household wastewater.

1 Introduction

The existence of heavy metals, particularly in drinking water, has gained extensive public attention in recent years (Joseph et al., 2019). Water pollution from natural processes and anthropogenic activities has potentially harmful consequences due to rapid transportation, bioaccumulation, and biomagnification of organic and inorganic pollutants from abiotic to biotic environments (Hassan et al., 2021). Toxic trace metals are non-biodegradable and remain in the environment for decades (El-Kady and Abdel-Wahhab, 2018). Hexavalent chromium (Cr(VI)), in particular, is the 17th most hazardous substance in the environment (Wise Jr et al., 2022) and is classified as Group 1 by the International Agency for Research on Cancer (IARC) owing to its carcinogenicity to humans (Braver-Sewradj et al., 2021). Consumption of hexavalent chromium-polluted water may result in health-related effects, such as brain, liver, and central nervous system damage (Braver-Sewradj et al., 2021). To reduce Cr(VI) discharge into the environment, the United States Environmental Protection Agency (US EPA) limits drinking water and groundwater to 0.05 and 0.5 mg.L⁻¹, respectively (Vaiopoulou and Gikas, 2020; Xia et al., 2020). Additionally, the World Health Organisation (WHO) and the South African National Standards (SANS241) set a maximum allowable level of 0.05 mg.L⁻¹ of chromium in drinking water (Ali Khan Rao et al., 2012; Nthunya et al., 2017).

The accelerated rate of industrialization, predominantly in the leather tanning, textile, electroplating, paint, and pigment production sectors, significantly contributes to the prevalence of Cr(VI) pollutants in aquatic environments (Dongre, 2021; Naja and Volesky, 2017; Guya, 2017). Innovative and contemporary water treatment technologies are available to normalize the unregulated discharge of these hazardous pollutants in water, whether from natural or anthropogenic wastewater sources (Hassan et al., 2021; Hassan et al., 2020a). Many heavy metal ion removal techniques include ion exchange, adsorbent separation, solar water evaporation, and electrochemical processes (Kordbacheh and Heidari, 2023; Koul et al., 2022). Other diverse treatment procedures include chemical precipitation, coagulation, and reverse osmosis (Saravanan et al., 2021; Elwakeel et al., 2020; Kerur et al., 2021). However, using standard procedures to remove heavy metals at low levels appears insufficient (Hassan et al., 2020b). Additionally, these methods generate chemical residues, toxic sludge, and concentrated brine, which can contaminate soil and water, disturb aquatic life, and damage the ecosystem and natural biodiversity (Manzetti and Van Der Spoel, 2015; Nishat et al., 2023).

The adsorption process, however, has the benefit of an easy and quick removal procedure; hence, it has a great potential for eliminating hexavalent chromium ions from wastewater (Chadha et al., 2022). Owing to its outstanding advantages, including high efficiency, adaptability, ease of design and operation, cost-effective regeneration, and inexpensive starting materials, adsorption technologies have attracted much interest in pollution remediation (Chadha et al., 2022; Varsha et al., 2022). For example, Rao et al. (2015) employed Artimisia absinthium. This medicinal plant material showed excellent adsorption in removing mutagenic Cr(VI) from aqueous solution along with Cu(II), Ni(II), and Zn(II). They achieved a Cr(VI) ions removal efficiency of 96% from their study at pH 2 (Rao et al., 2015). However, separating the adsorbent from the aqueous solution poses a challenge.

Numerous innovative adsorbents have been developed recently and functionalized with iron oxide nanoparticles (Fe₂O₃ and Fe₃O₄) to remove hazardous heavy metal ions from the human body (Hassan et al., 2020c). These iron oxide nanoparticles exhibit low toxicity, ease of synthesis, low cost, and large surface area (Subedi et al., 2019; Altun and Ecevit, 2020; Zhang et al., 2018; Neto et al., 2019). However, as adsorbents, their adsorption performance is limited due to their leaching under acidic environments and sluggish adsorption kinetics. Furthermore, due to the nano-range particle sizes of iron oxide nanoparticles, another separation step is needed to extract the adsorbent from the purified wastewater once the sorption process is complete. To overcome this challenge, polymeric matrix materials such as cellulose nanocrystals and capping agents are envisaged. Cellulose-based materials have been discovered to work well in removing impurities from contaminated water samples due to their affordability, re-usability, eco-friendliness, and highly successful surface modifications over synthetic polymers (Nishu Kumar, 2023). Thus, incorporating these novel properties is believed to enhance the removal of target pollutants (Rápó and Tonk, 2021).

The current study developed a low-cost adsorbent made of recycled pulp and paper sludge and Nylon-6 (N6) waste, modified with iron oxide nanoparticles (Fe₃O₄) and encapsulated with chitosan (CT). Cellulose nanocrystals (CNCs) were utilized since they possess a high surface caused by OH⁻ groups and their no-range, excellent mechanical properties, non-toxicity, and desirable chemical characteristics for adsorption processes (Babaei-Ghazvini et al., 2023). N6 is a synthetic polymer with high tensile strength, durability, compatibility, and chemical resistance (Karak, 2009; Shakiba et al., 2021). These properties made it an ideal matrix material to reinforce the nanocomposite adsorbent, ensuring mechanical stability and long-term durability. The nanocomposite was modified with Fe₃O₄ nanoparticles to increase the surface area and induce magnetic properties for ease of separation, enhanced easy recovery and regeneration of the adsorbent after heavy metal ion adsorption (Liosis et al., 2021; Khan et al., 2020).

Furthermore, chitosan (CT) possesses unique properties such as high porosity, biocompatible, reusable, sustainable, and high adsorption efficiency due to hydroxyl (−OH) and amino (−NH₂) groups (Sheth et al., 2021), making it an ideal capping agent to nanocomposite adsorbent. This work presents a novel method that employs waste-based materials (CNCs and N6), induced magnetic properties from Fe₃O₄ nanoparticles, and CT as a chelating agent. The novel adsorbents present excellent properties for the effective adsorption of heavy metal ions from aqueous solutions with their simple separation and recovery.

2 Materials and methods

2.1 Chemicals and reagents

Cellulose nanocrystals (CNCs) were prepared from recycled pulp and paper waste (Suter et al., 2024a; Suter et al., 2023b). Waste N6 was obtained from the Vaal University of Technology, Science Park campus-South Africa. Chitosan (C₅₆H₁₀₃N₉O₃₉), oxalic acid (C₂H₂O_4, ≥98%), hydrochloric acid (HCl ≥32%), potassium dichromate (K₂Cr₂O₇ >99%), sodium hydroxide (NaOH >98%), iron (III) chloride hexahydrate (FeCl₃.6H₂O, ≥99%), ammonium hydroxide (5N, NH₄OH, ≥25%), ferrous sulfate heptahydrate (FeSO₄.7H₂O, ≥99.5%), methanol (CH₃OH, ≥99.85%), molybdenum magnet and sulfuric acid (H₂SO₄, ≥99.85%), were purchased from Sigma-Aldrich, Meck-South Africa. All chemicals used were analytical grade and supplied without any further purification. The experiments were performed using deionized water from the Vaal University of Technology distillation column.

2.2 Preparation of the nanocomposite adsorbent

2.2.1 Synthesis of magnetic CNCs/N6 nanocomposite (CNCs/N6@Fe₃O₄)

The precursor N6 solution was prepared by mixing 14.0 g of shred N6 with a formic acid and acetic (1:1) acid solution at 80°C while continuously stirring at 150 rpm until a viscous solution was obtained. Afterwards, 10.0 g of CNCs was added gently to the resulting viscous solution and agitated under the same operating conditions for 1 h. The resulting CNCs/N6 viscous solution was quenched using 10-fold deionized water, filtered, and dried overnight at 50°C. The subsequent step involved crashing the dried CNCs/N6, then sieving using double mesh sieves with mesh sizes <50 μm to get finer particles. The obtained fine particles were packed in airtight plastic sample holders for the following procedure.

The magnetized cellulose nanocrystals/nylon−6 iron oxide nanocomposite (CNCs/N6@Fe₃O₄) was prepared using a modified method described in previous work (Suter et al., 2024a; Evans et al., 2022). Briefly, in a three-neck round-bottomed flask, a mixture containing 4.5 g of CNCs/N6 and 500 mL of deionized water was vigorously stirred at 200 rpm for 1 h at 80°C. The flask was fitted with a nitrogen circulation system to maintain an inert environment. After complete dispersion of CNCs/N6, 3.1 g of FeSO₄.7H₂O and 2.1 g of FeCl₃.6H₂O were added to the 80°C preheated mixture. It was followed by adding 5 N of NH₄OH dropwise while vigorously stirring until it turned dark brown. The stirring was continued for an additional 30 min. As stated in prior work (Suter et al., 2023b), the nanocomposite was collected using a molybdenum magnet and washed many times with deionized water. Finally, the resulting CNCs/N6@Fe₃O₄ nanocomposite was oven-dried for 24 h at 50°C.

2.2.2 Preparation of chitosan-encapsulated magnetic nanocomposite (CNCs/N6@Fe₃O₄−CT)

A modified method described by Sharififard and Rezvanpanah et al. (2021) and Mashile et al. (2020) was used to prepare the magnetic CNCs/N6@Fe₃O₄-chitosan nanocomposite (Mashile et al., 2020; Sharififard and Rezvanpanah, 2021). To prepare a solution of 0.2 M oxalic acid, 500 mL of deionized water was heated using a hot plate at 45°C–50°C, then gradually adding 2.5 g of chitosan while constantly stirring using a magnetic stirrer until a viscous gel formed. Then, 5.0 g of magnetized CNCs/N6@Fe₃O₄ nanocomposite was gently introduced and mixed for 2 h at 45°C–50°C and 150 rpm. After that, dropwise injections of the magnetic CNCs/N6@Fe₃O₄-chitosan mixture were made into a 0.7 M NaOH precipitation bath in order to create beads. The produced beads were filtered out of the NaOH solution and repeatedly washed with deionized water after they reached a neutral pH. The beads were ground into a fine powder and oven-dried overnight at 50°C for characterization and wastewater purification experiments.

2.3 Nanocomposite adsorbent characterization

The Fourier transform infrared spectroscopy (FTIR) analysis employed a Thermo Scientific Nicolet iS10 (Smart iTR) device with a diamond-based attenuated total reflectance (ATR) compartment was used to determine the chemical molecular structure of CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent before and after synthetic water treatment together with its precursor materials. The spectral resolution was set at 4 cm⁻¹ over 4,000 and 500 cm⁻¹ wavelength range. An average of 16 scans were captured for each spectrum, and the primary absorption peaks were identified. Surface morphology was examined using a scanning electron microscope (JEOL-IT 7500LA, Japan) with an accelerating 15–20 kV voltage. The samples were sputtered with a thin coating of gold metal after being placed on a carbon tape metal stub, then shattered in liquid nitrogen coated with gold to see the cross-sectional morphologies. A Shimadzu-XRD 700 X-ray diffractometer with Cu Ka radiation (λ = 0.1539 nm) was used to obtain the X-ray diffraction (XRD) patterns. The parameters for the equipment were 40 kV voltage, 30 mA current, and 1 Hz scan rate. Measurements were taken and recorded after the samples were loaded into the sample holder and placed on the XRD slot. Scherer’s Equation was used to calculate the crystal size (D), as indicated in Equation 1 below. This formula accounts for the X-ray wavelength (λ), the Braggs angle (θ), the dimensionless form factor (K), and the line broadening at half maximum intensity (FWHM) (β) (Monshi et al., 2012).

$CrystalSize\left(D\right)=\frac{\mathrm{K}\lambda }{\beta \mathit{Cos}\mathrm{\theta }}(1)$

A vibrating sample magnetometer (VSM) operating in the temperature range of 1.8–320 K and with a magnetic field of around ± 14 Tesla was used to measure the magnetic properties. The pH point zero charge (pH_PZC) for the Fe₃O₄, CNCs/N6@Fe₃O₄ and CNCs/N6@Fe₃O₄−CT were established using the pH drift equilibrium method. Briefly, 0.01 M NaCl stock solution was prepared, followed by 10 distinct 250 mL flasks filled with 50 mL of the solutions. The pH of the solutions was adjusted from 1 to 10 by adding 0.1 M NaOH/HCl solution dropwise, providing each flask with a distinct pH value. Each sample weighed 0.1 g and was put into different flasks with different pH values. The solutions were equilibrated for 48 h before the final pH was measured. The point of intersection was the pH_PZC of the prepared samples.

2.4 Batch adsorption experiments

Batch experimental studies were performed to determine the influence of various parameters, including the pH of the synthetic wastewater solution, nanocomposite adsorbent mass, metal ions concentrations, and the adsorption temperature. Hexavalent chromium solutions were made by dissolving potassium dichromate in double-distilled water. To create solutions with the necessary concentrations, a stock solution containing 1,000 mg/L was first prepared and then diluted to various solutions with different concentrations. The hexavalent ion solutions with the adsorbents under the multiple parameters to be investigated were shaken in a Merck Milipore-355 electric shaker at a 200 rpm shaking rate to attain equilibration.

The initial pH of the Cr(VI) solution adopted from preliminary results was adjusted from 1.0 to 12.0 using 0.1 M NaOH/HCl. The contact time (t) was varied from 1–150 min, starting metal ion concentrations ($C_o$) were set at 5–100 mg/L, with operating temperatures ranging from 298 to 318 K. The optimal pH value was used to test the effect of the adsorbent dose, with a Cr(VI) concentration of 20 mg/L and an adsorbent mass ranging from 0.025 to 0.30 g (1.0 g/L to 3.0 g/L) in 25 mL reactor.

The total Cr(VI) concentrations were measured using a Thermo Solaar S4 Atomic Absorption Spectrometer (AAS) at a wavelength of 357.9 nm under nitrous oxide using an air/acetylene flame. Before analysis, the mixtures were separated using a molybdenum magnet and filtered using Wathman No 1 filter paper. A 0.45 µm syringe filter followed this to prevent permeable particles from damaging the AAS aspirator. The obtained data was fitted to kinetics, isotherms and thermodynamics models.

The adsorbent performance was evaluated using Equations 2, 3 below to determine the amount of solute adsorbed per gram (removal capacity) of the nanocomposite (mg/g) at any time ($q_t$), and at equilibrium ($\left.q_e\right)$.

$q_t=\frac{\left(C_o-C_t\right)V}{m}(2)$

$q_e=\frac{\left(C_o-C_e\right)V}{m}(3)$

where V is the working solution volume (L), $q_t$ is the quantity of solute adsorbed (mg/g), C, and $C_{\mathrm{t}}$ represent the starting concentration and concentration at any time (t) in (mg/L), m is the mass of adsorbent, (g), and $C_e$ is the concentration at equilibrium.

The adsorbent percentage removal efficiency (%RE) was calculated using Equation 4, where $C_o$ represents the initial concentration and $C_e$ represents the final concentration in milligrams per litre (mg/L).

$RE=\frac{\left(C_o-C_e\right)}{C_o}\ast 100\%(4)$

2.5 Effect of salts/electrolytes on CNCs/N6@Fe₃O₄−CT adsorption process

Process effluents comprise various monovalent, divalent, and trivalent salts/electrolytes. Thus, the nanocomposite adsorbent was investigated in the presence of NaCl, CaCl₂, and FeCl₃ at optimal parameters obtained in Section 2.4 to ascertain its noble applicability.

2.6 CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent regeneration

To evaluate the re-usability of the nanocomposite adsorbent, separate 0.3 L solutions containing 20 mg/L of Cr(VI) were in contact with 0.30 g of the adsorbent for 120 min to ascertain sorption efficiency. After adsorption, the adsorbent was separated, rinsed with deionized water, and dried overnight. The loaded adsorbent was divided into three portions (0.10 g each). Each portion was then shaken using a Merck Milipore-355 electric shaker in one of the three desorption solvents: deionized water (H₂O), 0.1 M hydrochloric acid, and 0.1 M sodium hydroxide for 120 min. The supernatant solution was then filtered, and the used adsorbent was cleaned with deionized water and dried in an oven for reuse (three cycles). The Cr(VI) concentrations were analyzed in the residual solutions following adsorption and desorption experiments. Equation 5 was used to calculate the percentage efficiency of the desorption of metal ions.

$DE=\frac{C_d}{C_a}x100\%(5)$

where DE is the desorption efficiency, $C_d$ is the concentration desorbed, $C_a$ is the concentration adsorbed.

3 Results and discussions

3.1 Adsorbent screening

3.1.1 FTIR molecular structure determination

The FTIR spectra of the precursor materials and the nanocomposite adsorbent (CNCs/N6@Fe₃O₄–CT) before and after adsorption are shown in Figures 1A, B. From the infra-red characterization results, Figure 1A shows the tensile, flexural, and modified vibrations for O–H, C–H, and C–O corresponding to absorption bands of cellulose nanocrystals at wavelengths 2,922.42 to 3,427.65 cm^–1, 1,437.45 cm^–1, 1,379.66 cm^–1, and 1,043.75 cm^–1 respectively (Banza and Rutto, 2022; Tlou et al., 2023). Similarly, the absorption bands in N6 are linked to tensile vibrations at 3,296.17 cm^–1, 1,632.35 cm^–1, 1,537.39 cm^–1, 1,272.45 cm^–1, and 689.12 cm^–1 corresponding to N–H stretch, C=O stretch, N−H in-plane bend, C–N stretch, and N–H out-of-plane bend (Rodríguez et al., 2021). The flexural vibrations of the Fe–O group and the absorbed water for the Fe₃O₄ nanoparticles are linked to the absorption bands at 546.28 and 1,650.13 cm⁻¹. On the other hand, chitosan displayed absorption bands 3,229.68 cm^–1, 2,886.44 cm^–1, 1,635.10 cm^–1, 1,374.60 cm^–1, 1,021.19 cm^–1, and 897.99 cm^–1. Similar results were reported by (Smith, 2018) and are linked to various functional groups of chitosan, including O–H stretch, C–H bend, N–H bend, C–C in ring stretch, C–O stretch for alcohols, and N–H wag (1 and 2 amine), respectively (Claude and Onyango, 2024; Valentin et al., 2007).

Figure 1

Figure 1. (A) FTIR Spectra of CNCs/N6@Fe₃O₄-CT and its precursor materials and (B) CNCs/N6@Fe₃O₄-CT before and after adsorption.

Figure 1B shows most of the functional groups of precursor materials in the prepared nanocomposite. For example, the absorption bands for CNCs, N6, Fe₃O₄, and chitosan appeared in the nanocomposite at 3,282.60 cm^–1, 2,929.09 cm^–1, 2,861.26 cm^–1, 1,639.53 cm^–1, 1,537.39 cm^–1, 1,421.68 cm^–1, 1,028.27 cm^–1, 892.61 cm^–1, and 552.67 cm^–1 respectively, indicating successful incorporation into the adsorbent. Nonetheless, the functional groups shifted slightly to higher or lower wavelengths with widening/narrowing of some bands. After adsorption, it was noted that the functional groups did not undergo significant changes, as most peaks remained unaltered. This suggests that the material possesses exceptional characteristics and stability and has the potential for long-term re-usability.

3.1.2 Surface morphology

The SEM images in Figure 2 show the morphology of the produced nanocomposite adsorbent at different magnifications. It is evident from the SEM images that the nanocomposite adsorbent is composed of particles of various sizes and structures: finger-like, rod-like, flattened, string-like, spongy-like, and crystal-like structures. This suggests that the nanocomposite has a heterogeneous surface, which could contribute to enhanced surface area, porosity, and improved adsorption kinetics and adsorption capacity. Furthermore, it was observed that the adsorbent exhibits a porous surface with void patterns of varying sizes and shapes. This could imply that the nanocomposite material was successfully prepared and has a very high permeability.

Figure 2

Figure 2. SEM micro-graphs of CNCs/N6@Fe₃O₄-CT at different magnifications; (A) 100 μm, (B) 50 μm, (C,D) 20 μm.

3.1.3 Crystal properties

Figure 3 depicts the X-ray diffraction patterns for Fe₃O₄, CNCs/N6@Fe₃O₄, and CNCs/N6@Fe₃O₄−CT. It can be observed that the magnetite reflections (220), (311), (400), (422), (511), and (440) are the principal X-ray diffraction patterns at 2θ peaks, which are located at 30.09°, 35.56°, 43.15°, 53.40°, 57.33°, and 62.90° (Evans et al., 2022; Xu et al., 2013). These peaks correspond to JCPDS number 19–629 (Safitri et al., 2021) and suggest that the CNCs/N6@Fe₃O₄ and CNCs/N6@Fe₃O₄−CT nanocomposite incorporated the crystalline spinel-structured magnetite (Fe₃O₄) phase of iron oxide. The intense, distinct peaks indicate that the prepared materials were crystallized. The prepared Fe₃O_4, CNCs/N6@Fe₃O_4, and CNCs/N6@Fe₃O₄−CT had a particle size of 15.21 ± 2.91, 81.26 ± 1.56 and 103.34 ± 2.87 nm as calculated using the Scherer equation.

Figure 3

Figure 3. XRD pattern of Fe₃O₄, CNCs/N6@Fe₃O₄ and CNCs/N6@Fe₃O₄-CT.

3.1.4 Magnetic properties

The room-temperature magnetization curve gives a superparamagnetic state. The magnetite-based adsorbents Fe₃O_4, CNCs/N6@Fe₃O_4, and CNCs/N6@Fe₃O₄−CT showed the absence of coercivity and remanence (Figure 4). The CNCs/N6@Fe₃O₄-CT adsorbent saturation magnetization was established to be 26.90 emu/g, which is much lower than that of CNCs/N6@Fe₃O₄ (53.80 emu/g) and Fe₃O₄ (68.87 emu/g). The reported values were less than those for bulk Fe₃O₄, always within 92–100 emu/g (Cornell and Schwertmann, 2003). In the CNCs/N6@Fe₃O₄ and CNCs/N6@Fe₃O₄−CT, magnetic nanoparticles might have been “diluted” by diamagnetic physically adsorbed water, surface modifiers (CNCs, and N6), and the capping agent (CT), which significantly lowered the observed saturation magnetization. This could suggest that the capping agent and the surface modifiers may have altered the magnetic strength. Studies further report that the tiny size of Fe₃O₄ magnetic nanoparticles contributes to the value that is less than that of bulk Fe₃O₄ (Kodama, 1999). Small nanoparticles often have lower saturation magnetization values than bulk material of the same composition due to surface effects such as a magnetically deformed surface layer (Petrinic et al., 2021).

Figure 4

Figure 4. Room-temperature magnetization curve of Fe₃O₄, CNCs/N6@Fe₃O₄ and CNCs/N6@Fe₃O₄-CT.

3.1.5 Brunauer–Emmett–Teller (BET) surface properties

Brunauer–Emmett–Teller (BET) was performed to efficiently investigate the impacts of modifying materials on the adsorbents’ surface area. The data in Table 1 revealed that incorporating Fe₃O₄ increased the surface area of the nanocomposite. The same scenario was also observed with further addition of the capping agent (CT). The particles’ surface area was in the sequence of Fe₃O₄ >CNCs/N6@Fe₃O₄−CT > CNCs/N6@Fe₃O₄ >CNCs > N6. These findings demonstrated that adding CT and Fe₃O₄ boosted nitrogen adsorption, whereas CNCs, N6, inhibited it. The inclusion of magnetic iron oxide nanoparticles improved the nanocomposite adsorbent’s surface area, which is essential when determining whether an adsorbent is appropriate for water purification (Ayoade et al., 2020). However, the findings showed that Fe₃O₄ had wider pore openings, which were seen to narrow when incorporated into CNCs and N6. As a result, the surface pores increased when the nanocomposite was capped with CT. Consequently, even though CNCs and N6 reduced the pore sizes, the adsorbent sorption sites may still have been enhanced due to the addition of more functional groups that provide extra binding sites on the surface, as revealed by the FTIR data.

Table 1

Table 1. BET surface area and pore properties.

3.1.6 Zeta potential

Figure 5 illustrates zeta potential versus pH curves for Fe₃O₄, CNCs/N6@Fe₃O₄, and CNCs/N6@Fe₃O₄−CT. The isoelectric point (IEP) pH at zero point charge (pH_PZC) were found to be 6.7, 7.5 and 7.9 for Fe₃O₄, CNCs/N6@Fe₃O₄ and CNCs/N6@Fe₃O₄−CT, respectively. This is supported by research investigations by Lesiak-Orłowska et al. (2019) which concluded that when iron atoms coordinate with readily dissociable H−O−H molecules, surfaces composed of Fe₃O₄ become hydroxyl functionalized. These hydroxyl groups on the amphoteric surface react with bases or acids to create a pH_PZC that closely replicates neutrality. The charge on Fe₃O₄ surfaces can be positive or negative depending on the pH of the solution. When the surface is protonated below the isoelectric point, it generates ≡−FeOH²⁺, resulting in a net positive charge. However, when the surface hydroxyl groups are deprotonated, it produces ≡−Fe−O⁻ surface groups above the isoelectric point (Cristiano et al., 2011). The magnetic iron oxide nanoparticles are highly unstable because of their surface density charge (σ), which is too small in the IEP. This may result in the clumping of nanoparticles and flocculation. Studies from the literature indicate that the superficial charge must be altered using a variety of organic or inorganic surface modifiers to prevent agglomerations (Favela-Camacho et al., 2019). This can be observed from the changes in IEP because of the adsorbent modification with CNCs, N6, and CT. Furthermore, the observed IEP of 7.9 for CNCs/N6@Fe₃O₄−CT is linked to the IEP of CT, which is around 7.0–9.0. Notably, integrating CT into the composite resulted in high positive values for the zeta potential (Munim et al., 2020).

Figure 5

Figure 5. Zeta potential plot for Fe₃O₄, CNCs/N6@Fe₃O₄, and CNCs/N6@Fe₃O₄-CT

3.2 Effect of adsorption parameters

3.2.1 Effect of pH on the hexavalent chromium adsorption

Figure 6A displays the removal capacity of the adsorbents under various pH levels. From the experimental findings, Cr(VI) removal showed an increasing sorption tendency between pH 1.0 and 2.0, followed by a gradual decline from pH 3 to 12 for all the adsorbents. Studies from the literature indicate that in aqueous solutions with a pH range of 2.0–8.5, the bulk metal ions exist as ${\mathrm{M}}^{+/2+}$ or ${\mathrm{M}\left(\text{OH}\right)}^{+}$ ions (Li et al., 2020). Most metal ions produce ${\mathrm{M}\left(\text{OH}\right)}^{2+}$ above basic pH, leading to precipitation. Therefore, conducting adsorption experiments below pH 9.0 is always safe to prevent metal ion precipitation. Similarly, the concentration of H₃O⁺ ions is substantially larger and exceeds that of metal ions at very low pH values. Excessive H₃O⁺ ions cover the active sites of the adsorbent, allowing the metal ions to move about freely in the solution (Cruz-Lopes et al., 2021), translating to competing reactions between the adsorbate metal ion and H₃O⁺ ions. As the pH of the solution becomes more basic, there is less competition, and positively charged metal ions stick to available binding sites on the adsorbent (Dhumal and Sadgir, 2023). Furthermore, pH_PZC is vital in increasing adsorption capacity at high solution pH. However, this was different with Cr⁶⁺, which demonstrated greater adsorption effectiveness at pH 2.0 (Figure 6A) with adsorption capacity of 3.93, 4.52, 11.72, 14.00 and 16.41 mg/g for N6, CNCs, Fe₃O₄, CNCs/N6@Fe₃O₄, and CNCs/N6@Fe₃O₄−CT respectively.

Figure 6

Figure 6. (A) Effect of solution pH on adsorption process at the optimal contact time of 120 min, initial metal ion concentration of 20 mg/L, adsorbents dosage of 1.0 g/L, shaking rate of 200 rpm at 298 K, (B) Stability of various hexavalent chromium species under different pH conditions.

According to the literature, the stability of various hexavalent chromium species in solution depends greatly on the pH of the solution (Ouma et al., 2022). These species include chromic acid (H₂CrO₄), hydrogen chromate (HCrO⁴⁻), dichromate (Cr₂O₇²), and chromate (CrO₄²⁻), which are formed as the pH of the solution rises from pH 1.0–6.0 (Figure 6B) (Unceta et al., 2010). At acidic pH, the high concentration of hydrogen ions protonates the surface binding sites of the adsorbent, causing the negatively charged Cr(VI) species to be electrostatically attracted and bound to the adsorbent surface. This causes the hexavalent chromium ions to be removed from the synthetic wastewater. The ions may stay bound by electrostatic forces, or a reduction on the surface of the adsorbent may follow this sorption. The pH of 2.0 was determined to be optimal because the majority of the Cr(VI) ions in the solution were transformed to Cr(III) at pH 1.0 (Pfeifer and Skerget, 2020). Equations (6–9) below illustrate the Cr(VI) adsorption mechanisms onto CNCs/N6@Fe₃O₄−CT.

Equations 6, 7 represent the electrostatic attraction and complexation with functional groups from CNCs and N6.

$\mathrm{R}–\text{OH}+{\text{Cr}}_2{\mathrm{O}}_7^{2-}\to \mathrm{R}–\mathrm{O}–{\text{Cr}}_2{\mathrm{O}}_7^{2-}+{\mathrm{H}}^{+}\left(\text{Electrostatic\hspace{0.17em}attraction}\right)(6)$

$\mathrm{R}–{\text{NH}}_2+{\text{Cr}}_2{\mathrm{O}}_7^{2–}\to \mathrm{R}–{\text{NH}}_2.{\text{Cr}}_2{\mathrm{O}}_7^{2–}\left(\text{Complexation}\right)(7)$

where R-represents the main matrix of CNCs/N6@Fe₃O₄−CT nanocomposite.

Equations 8, 9 represent the partial reduction of Cr(VI) to Cr(III) due to the presence of Fe₃O₄.

${\text{Cr}}_2{\mathrm{O}}_7^{2–}+14{\mathrm{H}}^{+}+6{\mathrm{e}}^{–}\to 2{\text{Cr}}^{3+}+7{\mathrm{H}}_2\mathrm{O}(8)$

${\text{Fe}}_3{\mathrm{O}}_4+8{\mathrm{H}}^{+}+{\text{Cr}}_2{\mathrm{O}}_7^{2–}\to 2{\text{Fe}}^{3+}+{\text{Cr}}^{3+}+4{\mathrm{H}}_2\mathrm{O}(9)$

The significant improvement in adsorption capacity was due to improved surface area and a greater number of functional groups on the adsorbent surfaces due to the addition of Fe₃O₄ and CT. This is confirmed by the FTIR and BET results (Figure 1A; Table 1). The surface groups of both Fe₃O₄ and CT are amphoteric, functioning as acids or bases depending on the solution pH (Mourya et al., 2010).

3.2.2 Effect of adsorbent dosage

Adsorbent dosage is vital to adsorption since large-scale adsorption columns are designed and optimized based on adsorbent adsorption capacity. In this study, the adsorbents dosage was varied from 1.0 g/L to 3.0 g/L while the Cr⁶⁺ ions concentration was set at 20 mg/L, shaking speed of 200 rpm at room temperature of 298 K with optimal adsorption pH of 2.0. The findings are shown in Figures 7A, B. From Figure 7A, an apparent reduction in the adsorption capacity with an increase in adsorbent dosage was observed with all adsorbents. The obtained adsorption capacities showed a decline from 67.12 to 4.87 mg/g, 65.12 to 4.42 mg/g, 47.26 to 1.26 mg/g, 33.96 to 0.41 mg/g, and 31.06 to 0.15 mg/g for N6, CNCs, Fe₃O₄, CNCs/N6@Fe₃O₄, and CNCs/N6@Fe₃O₄−CT respectively. This is explained by the fact that there is a fixed number of potential adsorbent sites for the entire adsorption system per unit mass of total adsorbent; therefore, increasing the adsorbent’s mass will probably lead to a decrease in both the amount of active sites per mass of adsorbent and adsorption capacity. In addition, increasing the adsorbent dose reduces the solute concentration difference between the surface and bulk of the adsorbent (McKinley and Jenne, 1991). Studies also reveal that the adsorbent particles interact with one another, and any system with excess solids may occasionally block potential active sites, resulting in a decreased adsorption capacity. However, as the adsorbent dosage increases, the amount of metal ions removed increases (Qasem et al., 2021). This was evident by the increased percentage removal efficiency for all adsorbents, with CNCs/N6@Fe₃O₄−CT posting a rise from 1.20% to 21.56%, 2.66%–30.54%, 35.22%–78.71%, 47.51%–89.31% and 62.97%–98.24% for N6, CNCs, Fe₃O₄, CNCs/N6@Fe₃O₄, and CNCs/N6@Fe₃O₄−CT respectively. Consequently, despite greater adsorbent doses yielding more active sites, the relationship between active sites per unit mass of adsorbent and percentage removal rate is not significant. Therefore, utilizing a smaller dosage of CNCs/N6@Fe₃O₄−CT and more concentrations of Cr(VI) ions is recommendable for improved removal capacity. The prepared adsorbent CNCs/N6@Fe₃O₄−CT was thus selected for optimization with increased Cr(VI) ion concentration.

Figure 7

Figure 7. Effect of adsorbent dosage; (A) Cr(VI) adsorption capacity and (B) Cr(VI) removal efficiency.

3.2.3 Effect of initial Cr(VI) concentration adsorption using CNCs/N6@Fe₃O₄−CT

In this section, the working concentrations of Cr(VI) ions were 5, 10, 20, 50, 75, and 100 mg/L. The other experimental variables were set at predefined conditions: room temperature (298 K), pH of 2.0, shaking rate of 200 rpm, 150 min contact time, and adsorbent dosage of 1 g/L. Figures 8A, B) shows the adsorption capacity profile plots at different adsorbent concentrations. The findings showed an enhanced adsorption capacity from 4.31 mg/g to 45.43 mg/g as the hexavalent chromium concentrations increased from 5 to 100 mg/L (Figure 8A). The adsorbent removal rate was rapid within the first 30 min, increased sharply to around 90 min, and then gradually stagnated after approaching equilibrium at 120 min (Figure 8A). The rapid sorption of the nanocomposite adsorbent could have been attributed to adsorbent’s hydrophilicity, conformational flexibility of various functional groups, larger surface area, and a more significant number of exchange sites, which are seen to decrease with time. Studies from the literature reported that the initial higher rate results from more sorption and binding sites being available on the adsorbent surface (Burtch et al., 2014). At the more reactive sites, metal ions are adsorbed, and the adsorption process grows more complex, making the sorption process less favourable as these sites are gradually occupied. Additional studies in the literature have confirmed that the adsorption of metal ions conforms to these general characteristics (Ugbea et al., 2020).

Figure 8

Figure 8. Effect of initial metal ion concentration. (A) Cr(VI) adsorption capacity and (B) Cr(VI) removal efficiency.

Furthermore, the studies indicate that concentration gradient roses with an increase in the initial metal ion concentration, which improves adsorption, but mass transfer resistance decreases removal efficiency (Rudi et al., 2020). From this study (Figure 8B), the CNCs/N6@Fe₃O₄−CT ability to remove Cr(VI) ions was reduced since there was greater competition among the excess Cr(VI) ions to adhere to an adsorbent surface. This can be confirmed by the decrease in removal efficiency from 98.95% to 43.60% when the initial metal ion concentration was increased from 5 mg/L to 100 mg/L.

3.2.4 Effect of system temperature on adsorption of Cr(VI) ions using CNCs/N6@Fe₃O₄−CT

The effect of temperature on the adsorption of Cr(VI) ions using CNCs/N6@Fe₃O₄−CT was evaluated at a contact time of 120 min, initial Cr(VI) concentration of 20 mg/L, adsorbent dosage of 1.0 g/L, shaking rate of 200 rpm, and a pH 2.0. The findings in Figure 9 obtained adsorption capacity of 15.10, 16.21, 16.84, 17.02, and 17.04 mg/g, while the adsorption efficiency was 81.71%, 87.70%, 91.13%, 92.07%, and 92.23% at 298 K, 303 K, 308 K, 313 K and 318 K respectively. The adsorption capacity and efficacy of Cr(VI) ions removal by CNCs/N6@Fe₃O₄−CT were positively impacted by temperature but with a minimal difference. These findings suggest that a significant amount of Cr(VI) ions may be removed using the CNCs/N6@Fe₃O₄−CT nanocomposite at room temperature. These results agree with studies from the literature, which indicated that the adsorption of metal ions using biomass and iron-based adsorbents is minimally influenced by system temperature (Ogunlalu et al., 2021).

Figure 9

Figure 9. Effect of temperature on hexavalent chromium removal.

3.3 Adsorption kinetics and mechanism of adsorption

Comprehending the kinetic interactions in adsorption reactions is essential in identifying rate-controlling steps. This understanding helps explain the factors that influence molecular interactions, such as the molecules’ molecular morphology, reaction temperature, and polarity. It also enables the prediction of system functionality under various reaction environments and during scale-up procedures. The mathematical expressions employed in this study adopted linear models of the Pseudo-first-order (PFO), Pseudo-second-order (PSO), Elovich, and Intra-particle diffusion models.

3.3.1 PFO kinetic model

The PFO was used to evaluate the kinetic behaviour and comprehend the Cr(VI) interaction mechanism with CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent. PFO linear fit findings are presented in Figure 10A and Table 2. The generalized integral nonlinear form of the PFO kinetic model can be stated as per Lagergren, 1898 as provided in Equation 10.

$\frac{dq\mathrm{t}}{d\mathrm{t}}=K_1\left(q_{\mathrm{e}}-q_{\mathrm{t}}\right)(10)$

Figure 10

Figure 10. (A) Pseudo-first-order kinetic model linear fit and (B) Pseudo-second-order kinetic model linear fit for Cr⁶⁺.

Table 2

Table 2. Summary of kinetic model parameters.

By integrating Equation 10 at the boundary conditions, t = 0, $q_{\mathrm{t}}$ = 0, and at t = t (>0), $q_{\mathrm{t}}$ = $q_{\mathrm{t}}$, the linear form Illustrated in Equation 11 is obtained.

$\mathit{ln}\left(q_e-q_{\mathrm{t}}\right)=\mathit{ln}{q}_{\mathrm{e}}-K_{1}t(11)$

where $q_{\mathrm{t}}$ and $q_e$ variables represent the quantities of solute adsorbed at any time (t) and the amount adsorbed at equilibrium time (t) in (mg/g), $K_1$ reflect the first-order rate constant (min⁻¹), and t is the contact time (min). The theoretical value of $q_e$ and the rate constant $K_1$ are obtained from the slope and intercept of the linear plot of ($q_e-q_t$) vs. t.

From the model fit, the linear regression coefficients (R²) were comparatively low and ranged from 0.781 to 0.922; this indicated that the model was inappropriate in predicting the current adsorption mechanism Cr(VI) onto CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent. The weak linear regression coefficient (R²) and extremely low values of $q_{\text{cal}}$ provide more confirmation of the model’s failure.

3.3.2 PSO kinetic model

The findings on the applicability of the pseudo-second-order kinetic model for the study are presented in Figure 10B and Table 2. Equation 12 represents the Pseudo–second–order nonlinear kinetic mathematical expression.

$\frac{d{q}_t}{dt}=K_2{\left(q_{\mathrm{e}}-q_t\right)}^2(12)$

By integrating Equation 12 above, the linearized form is provided in Equation 13.

$\frac{t}{q_{\mathrm{t}}}=\frac{1}{K_2{q}_e^2}+\frac{t}{q_e}(13)$

The slope $\frac{1}{q_t}$ and the intercept $\frac{1}{K_2{q}_e^2}$ is obtained by plotting $\frac{t}{q_t}$ vs. t while the theoretical value of ${\mathrm{q}}_{\mathrm{e}}$ (mg/g) and the second-order rate constant K₂ (g/mg.min) is determined from the slope and intercept. Additionally, Equation 14 provides the initial sorption (h) rate value at t → 0.

$h=K_2{q}_e^2(14)$

For validation of the models with the experimental data, an error function (χ) can be determined using Equation 15 below.

${\mathrm{\chi }}^2=\sum \frac{\left(q_e\left(\mathit{exp}\right)-q_e{\left.\left(calc\right)\right)}^2\right.}{q_e\left(calc\right)}(15)$

The suitability of the kinetic model on the adsorption of Cr(VI) using CNCs/N6@Fe₃O₄−CT was assessed using the linear regression coefficient (R²) and the error function (χ). The experimental data under various physio-chemical conditions fitted well to the PSO linear kinetic model. The model displayed high linear regression coefficient values (R² > 0.995). In addition, the error function’s low amplitude demonstrated the model’s suitability. This confirms that electron sharing between the solute and adsorbent occurred (Equations 6–9).

Furthermore, since there was less competition between the metal ions and the adsorption site at the higher initial concentration, lower K₂ values were obtained. Numerous studies on the removal of heavy metals, including zinc (Zn²⁺), copper (Cu²⁺), and nickel (Ni²⁺), demonstrated that adsorption systems followed PSO kinetics models (Biswas et al., 2019; Singh and Mishra, 2022; Singh and Mishra, 2021). The results from this study further suggest that the process is not governed by a single-step metal ion adsorption on the CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent, and that entails several steps. These results agree with the findings reported by Salehi et al. (2021).

3.3.3 Elovich model

The kinetics of chemisorption on heterogeneous adsorbents is evaluated using the Elovich model. The model predicts the existence of heterogeneous active surface sites with various activation energies (Wang and Guo, 2023). The Elovich model is described by Equation 16, where $\alpha$ (mg.g⁻¹min⁻¹) is the rate of chemisorption at zero coverage, and $\beta$ (g.mg⁻¹) is the desorption constant.

$q_t=\left(\beta \text{lnt}+\beta \mathit{ln}\alpha \right)(16)$

The linearized form of the Elovich mathematical expression is given in Equation 17.

$q_t=\frac{1}{\beta }\ln \left(1+\alpha \beta t\right)(17)$

Figure 11 and Table 2 illustrate how the model’s linear fit to the experimental data permitted the derivation of the Elovich constants and linear correlation coefficient. The plot of $q_t$ vs. ln(t) yields a linear relationship with a slope of (1/β) and an intercept of (1/β) ln (αβ). According to the Elovich model’s coefficient of determination, sorption onto the CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent was chemically guided as shown in (Equations 6–9) since R² > 0.96. Similarly, high initial sorption rates compared to corresponding desorption constants suggested that the nanocomposite adsorbent effectively adsorbed the metal ions (Chaudhary et al., 2019). In the Elovich model, active surface sites are assumed to be heterogeneous and to have a range of activation energies. The adsorbents had initial sorption rates alpha that were greater than the desorption constant beta, implying that it is suitable for the adsorption of metal ions. This is in agreement with studies reported in the literature (Rani et al., 2022).

Figure 11

Figure 11. Elovich kinetic model linear fit.

3.3.4 Intra-particle diffusion model

The intraparticle diffusion model (Webber-Morris model) is crucial for understanding the behaviour of any adsorption process (Weber and Morris, 1963; Lesiak-Orłowska et al., 2019; Ouma et al., 2022). Understanding the adsorption mechanism and how it underlies the system’s apparent dynamic behaviour is essential for designing and controlling any large-scale adsorption system. The rate-limiting phases or mechanisms of the adsorption process can be identified using this model. The model’s mathematical form is given in Equation 18.

$q_t=K_{id}{t}^{1/2}+C(18)$

where $q_t$ is the amount of solute adsorbed at any time, t, (mg/g), $t^{0.5}$ is the square root of time (${\mathit{min}}^{0.5}$) and $K_{id}$ in (mg/g.min^0.5) is the intraparticle diffusion coefficient, respectively. The linear plot of $q_t$ against $t^{0.5}$ yields the slope (K_id) and intercept (C).

According to the results of this study, as shown in Figure 12; Table 2, the hexavalent chromium ions were initially adsorbed quickly until saturation. The rate-limiting step is frequently predicted using this model for an adsorption system involving porous materials. The model states that an entire system is controlled by intraparticle diffusion when the plot between $q_t$ and $t^{0.5}$ results in a straight line that goes through the origin. However, the plot in this study did not result in a single straight line passing through the origin. From the linear plot (Figure 12), the following steps may be listed as the adsorption mechanism: metal ion transport from the bulk solution to the adsorbent surface; diffusion across the adsorbent surface’s boundary layer; adsorption on the adsorbent surface’s active site and through intra-particle diffusion into the adsorbent’s pores. In addition, the slowest step, external film diffusion, or pore diffusion, regulates the total adsorption rate. Studies from the literature reveal that in most situations, external film diffusion can heavily influence the entire adsorption process (Pholosi et al., 2020).

Figure 12

Figure 12. Intraparticle diffusion kinetic model linear fit.

3.4 Isotherm studies

Isotherm studies predict the interactions between adsorbate molecules and the active sites on the sorbent surface. Furthermore, it offers helpful information regarding the quantity of adsorbate removed and the concentration of adsorbate at equilibrium. In technical applications such as water treatment systems, a thorough understanding of the behaviour of adsorbates and adsorbents is critical. In this study, the performance of CNCs/N6@Fe₃O₄−CT on the adsorption of Cr(VI) ions was predicted by the Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich (D–R) linearized isotherm models. The coefficient of determination (R²) was used to assess the models’ superiority in predicting the adsorption of Cr(VI) ions onto CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent.

3.4.1 Langmuir isotherm model

The Langmuir isotherm model assumes that the rate-limiting reaction step is a surface reaction with the same adsorption activation energy at each active site, comparable to heterogeneous catalytic reactions. Furthermore, the model presupposes that the metal ions and previously adsorbed molecules would not interact in any way (Langmuir, 1918). Equation 19 below provides the Langmuir mathematical expression in its non-linearized version.

$q_e=\frac{q_m{K}_L{C}_e}{1+K_L{C}_e}(19)$

Equation 20 shows the linearized form of the Langmuir isotherm mathematical model.

$\frac{1}{q_e}=\frac{1}{q_m{K}_L{C}_e}+\frac{1}{q_m}(20)$

Where $q_m$ is the maximum adsorption capacity of the adsorbent (mg/g), and $K_L$ is the energy of adsorption (L/mg).

A straight line with a slope of $\frac{1}{q_m}$ and an intercept of $\frac{1}{K_L}$ is produced by plotting $\frac{1}{q_e}$ vs. $\frac{1}{C_e}$. This isotherm also yields the dimensionless separation factor, $R_L$, which determines if the adsorption process is viable at various starting solute concentrations (Abate et al., 2020). The separation factor for the dimensionless parameter may be computed using Equation 21 below.

$R_L=\frac{1}{\left(1+K_L{C}_o\right)}(21)$

If (R_L > 1 or R_L = 1), then the system is unfavourable but considered favourable if (0< R_L <1). Furthermore, R_L = 1 and when R_L = 0 describe a linear and irreversible system, respectively.

From the graphical plots (Figure 13), the Langmuir model better predicted the adsorption of Cr(VI) ions onto CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent with a coefficient of determinant R² > 0.99, as shown in Table 2. This suggested that the monolayer adsorption process proceeded on the homogeneous surface of the adsorbent. The dimensionless separation factor R_L values calculated were within the expected range of 0< R_L <1 (0.835–0.920), thus confirming that the adsorption process was favourable.

Figure 13

Figure 13. Langmuir linear fit isotherm model.

3.4.2 Freundlich isotherm model

According to the Freundlich isotherm model, multilayer adsorption occurs on the adsorbent’s heterogeneous surface. The model also predicts that as the concentration of metal ions is increased, the quantity of the metal ions adsorbed increases (Freundlich, 1906). The nonlinear mathematical expression of the isotherm model is illustrated in Equation 22 below.

$q_e=k_f{t}^{1/n}(22)$

The linearized form of the Freundlich isotherm model is shown below in Equation 23.

${lnq}_e=\frac{1}{n}\mathit{ln}{C}_e+\mathit{ln}{K}_f(23)$

Where $C_e$ is the concentration of metal ions at equilibrium (mg/L), $q_e$ is the quantity of solute adsorbed at equilibrium (mg/g), while ${\mathrm{K}}_f$ and n are Freundlich constants. The plot of ${lnq}_e$ versus ${lnC}_e$ results in a straight line with the slope $\left(\frac{1}{n}\right.$ ), n is the degree of system heterogeneity) and an intercept $\left({\mathrm{K}}_f\right)$ is the system’s adsorption capacity. For favourable adsorption, n should be greater than 1.

From the study’s findings in Figure 14, the Freundlich isotherms linear fit plot produced a straight line. The graphical representation demonstrates that the Freundlich model has a linear regression value (R² > 0.98), as given in Table 3. The results showed that multilayer adsorption of Cr(VI) ions onto CNCs/N6@Fe₃O₄−CT occurred due to the relatively high regression coefficient. Studies indicate that the adsorption process on a nanocomposite adsorbent is effective if the Freundlich constant(n) value is between 1 and 10 (Sadegh et al., 2018). According to the experimental data in Table 3, the Freundlich constants ranged from 3.8366 to 4.7043, which is within an acceptable range and suggests that the adsorption process was effective, thus making the nanocomposite adsorbent a good choice for metal ions adsorption.

Figure 14

Figure 14. Freundlich linear fit isotherm model.

Table 3

Table 3. Summary of isotherms and thermodynamic model parameters.

3.4.3 Temkin isotherm model

The Temkin isotherm assumes a linear rather than a logarithmic reduction in the heat of adsorption, disregarding deficient and very high concentrations. The model also assumes a homogeneous distribution of bonding energy up to a specific maximum bonding energy and that the interactions between the adsorbent and metal ions would result in a linear decrease in the heat of adsorption with surface coverage. (Temkin, 1941). Equation 24 provides the mathematical expression for the Temkin isotherm model.

$q_e=\frac{RT}{B}{lnK}_T+\frac{RT}{B}{lnC}_e(24)$

The linearized form of the Temkin isotherm is given below in Equation 25.

$q_e=\frac{RT}{B}{lnK}_T{C}_e(25)$

where $B$ is the Temkin constant related to the heat of adsorption (J.mol⁻¹), $T$ is the absolute temperature (K), $R$ is the gas constant (8.314 J/mol K), and K_T is the equilibrium binding constant corresponding to the maximum binding energy (L/mol). The plot of qe vs. ln (C_e) results in a straight line with slope (RT/B) and intercept (RT lnK_T)/B.

From Figure 15, the interaction between the Cr(VI) ions and CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent was moderate to strong. This could have been attributed to the intense heat of adsorption as indicated by the B values ranging from 3.5483 to 5.0568 J/mole. The linear correlation coefficient (R² > 0.96) further demonstrated the strong interaction, indicating that the model fitted the experimental data. The adsorption energy recorded (E < 8.0 J/mole) and high R² value suggest that the Temkin isotherm is a suitable model for describing the adsorption process of Cr(VI) ions onto CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent. This further indicates intense contact between Cr(VI)ions and the surface of the nanocomposite adsorbent.

Figure 15

Figure 15. Temkin isotherm linear fit.

3.4.4 The Dubinin–radushkevich (D–R) isotherm model

The D–R isotherm model was developed to determine the typical porosity and the apparent free energy of adsorption. The isotherm model, however, does not assume a uniform surface or a constant sorption potential (Adewumi et al., 2012). It makes it possible to differentiate between chemisorption and physisorption. The Dubinin−Radushkevich isotherm, Equation 26, provides a three-parameter equation to describe data on solute adsorption on heterogeneous surfaces.

$q_e=q_s{e}^{{-K}_{ad}{\epsilon }^2}(26)$

where q_s is the theoretical isotherm saturation capacity (mg. g⁻¹), K_ad is a constant related to the free energy of adsorption per mole adsorbate (mol². J^–2), ε is the Polany potential associated with the equilibrium concentration (Equation 27), $R$ is the gas constant 8.314 in kJ/mol.K, $T$ is the temperature in Kelvin. C_e is the equilibrium concentration in solution (mg/L).

$\epsilon =RTln\left(1+\frac{1}{C_e}\right)(27)$

The combination of Equations 26, 27 yields a linearized form, as given in Equation 28 below.

${lnq}_e={lnq}_{\mathit{max}}{-K}_{ad}{\epsilon }^2(28)$

where $q_e$ is the number of ions adsorbed in (mg/g), $q_{\mathit{max}}$ is the capacity of the D−R monolayer (mg/g), K_ad is the energy constant in (mol²/kJ²).

From the plot of $\mathit{ln}{q}_e$ versus ${\epsilon }^2$, the slope $K$ (mol²/kJ²) is obtained while $q_{\mathit{max}}$ (mg/g) yields the intercept, which represents the adsorption capacity (Figure 16).

Figure 16

Figure 16. Dubinin–Radushkevich Linear fit isotherm model.

The energy required to remove each molecule of metal ion from the solution to the adsorption site can be calculated using Equation 29.

$E=\frac{1}{\sqrt{2K_{ad}}}(29)$

The values of mean free energy (E) are used to predict the adsorption mechanisms. When the E is ≤8 kJ/mol, it suggests a physical adsorption interaction, whereas a value E between 8 and 16 kJ/mol suggests an ion exchange mechanism. The present study’s numerical adsorption values were between 10.00 and 10.660 kJ/mol, corresponding to the chemical process (Table 3). This supports the adsorption mechanisms of Cr(VI) onto CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent provided in Equations 6–9.

3.5 Adsorption thermodynamics and nature of adsorption

In adsorption thermodynamics, system characteristics such as the Gibbs free energy change $\left(∆G^o\right)$ (kJ/mole), enthalpy change $\left(∆H^o\right)$ (kJ/mole), and entropy change $∆S^o$ (J/mol.K) are crucial in determining the energy state (exothermic or endothermic process), the degree of reaction disorder and spontaneity (Al-Harby et al., 2021; Saha and Chowdhury, 2011). Equations 30–33 below provide mathematical expressions for various thermodynamic parameters.

$K_c=\frac{q_e}{C_e}(30)$

$∆G^o=-RTln{K}_c(31)$

$∆G^o=∆H^o-T∆S^o(32)$

$\mathit{ln}{K}_c=\frac{∆S^o}{RT}-\frac{∆H^o}{RT}(33)$

where $T$ is the temperature in kelvin (K), $R$ is the universal gas constant (8.314 J/mol.K), $q_e$ is the solid-phase equilibrium concentration (mg/L), and C_e is the equilibrium solution concentration (in mg/L). The values of $∆{\mathrm{H}}^{\mathrm{o}}$ and $∆{\mathrm{S}}^{\mathrm{o}}$, are obtained from the slope and the intercept from the linear plot of ln (K_c) vs. 1/T (K⁻¹).

If ΔG° < 0, the adsorption process is said to be thermodynamically favoured; if ∆H° is negative, the adsorption process is exothermic and spontaneous, and if ∆S ° is positive, it indicates increasing randomness at the solid solution interface during adsorption.

From Figure 17 and Table 3, the thermodynamic findings suggest that Cr(VI) adsorption onto CNCs was favourable with ΔG°(−1,596.85 kJ/mole). It also revealed a spontaneous adsorption process with negative ∆H° values, implying that it may occur without requiring extra energy. The adsorption was exothermic, as shown by the negative value of ∆H° (−826.521 kJ/mole). Furthermore, the positive value of ∆S° (2.585 kJ/mole) demonstrated the CNCs/N6@Fe₃O₄−CT affinity for Cr(VI) ions at the solid-liquid interface. This confirms the increasing randomness at the solid solution interface during adsorption. With increasing temperature, a slight change in the negative values of ΔG suggests that the adsorption process is less advantageous at high temperatures and preferable at low temperatures. These findings agree with thermodynamic studies from similar investigations in the reviewed literature (Mennas et al., 2023; Munagapati et al., 2022).

Figure 17

Figure 17. Adsorption thermodynamic plot.

3.6 Effect of the presence of salts/electrolytes

Process effluent commonly comprises monovalent, divalent, and trivalent salts. As a result, it was crucial to understand how well the CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent performs in such salts before treating any actual industrial effluent. Herein, it was investigated how well the CNCs/N6@Fe₃O₄−CT composite adsorbent performs in Cr(VI) adsorption in the presence of NaCl, CaCl₂, and FeCl₃ at 20 mg/L each. From the experimental data illustrated in Figure 18, the percentage removal of Cr(VI) decreased to 70.90%, 58.64% and 44.05% in the presence of NaCl, CaCl₂, and FeCl₃ salts, respectively. The findings suggest that the additional salt species could have interfered with the ability of the Cr(VI) ions to bind to the active sites on the CNCs/N6@Fe₃O₄−CT surface, lowering its adsorption efficiency. Zhang et al. (2019) indicated that ionic strength also affects the equilibrium constant of the bulk liquid and adsorbent contact, which is crucial to the entire adsorption process (Zhang et al., 2019). The total metal ion’s size, atomic radius, and molecular weight also significantly affect how nanocomposite adsorbent adsorbs (Zhang et al., 2019). The impact of divalent and trivalent ions on transition metals is more significant than that of monovalent ions because Fe³⁺ contains more electrons in the outer orbit than earth metals, forming a surface complex with the adsorbent surface than Ca²⁺ and Na (Liu et al., 2016).

Figure 18

Figure 18. Effect of the presence of salts/electrolytes on adsorption of Cr⁶⁺.

3.7 CNCs/N6@Fe₃O₄−CT adsorbent regeneration

The percentage desorption and adsorption efficiency of Cr(VI) were evaluated and presented in Table 4. From the experimental findings, water recorded the lowest desorption capacity, less than 10% in the first cycle. HCl and NaOH eluates produced more than 50% of their desorption capacities after two cycles before starting to drop. However, NaOH was identified to be more effective due to its excellent desorption efficiency, recording the highest among the three chosen desorption eluents. Studies show that when OH⁻ions from basic solutions are present, they create chromate ions (CrO₄^2–), which are subsequently released into the solution when OH⁻ions take their place on the adsorbent surface (Herath et al., 2021; Liao et al., 2022). Furthermore, the decline in the re-usability of the adsorbent was assumed to be a result of permanent or semi-permanent complex development (Equation 7), which makes desorption difficult. Given that, the prepared CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent can be utilized for more than two cycles, and is made of low-cost materials (waste-based), and efficient synthesis procedure has the potential for optimization and application in domestic and industrial wastewater treatment.

Table 4

Table 4. Regeneration of CNCs/N6@Fe₃O₄−CT composite adsorbent using different eluents.

4 Conclusion

The effectiveness of prepared CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent in removing Cr(VI) metal ions is reported in this work. BET results showed that the nanocomposite material presents excellent porosity with a large surface area. FTIR spectra revealed the incorporation of functional groups from CNCs, N6 Fe₃O₄ and CT. The adsorption experiments showed that the adsorbent presented good sorption efficiencies at pH 2. An increased operating temperature minimally impacted the adsorption process, while the presence of ionic salts/electrolytes decreased the percentage removal for hexavalent chromium. NaOH was the best eluate due to its excellent desorption efficiency. The experimental data fitted well with the pseudo-second-order kinetic model. Furthermore, the isotherm models established that the Langmuir monolayer isotherm effectively predicted the adsorption process, which was confirmed by the R_L values (0< R_L <1). The order of isotherm model fitting was Langmuir > Freudlich > D−R > Temkin. The Gibbs free energy showed that the adsorption was favourable and spontaneous and that exothermic processes governed the adsorption process. Chemical adsorption dominated the process, with ion exchanges and the occurrence of chemical interactions. Given that the prepared CNCs/N6@Fe₃O₄−CT nanocomposite adsorbent was utilized for more than two cycles, this polymeric ferromagnetic nanocomposite adsorbent from low-cost materials (waste-based), biodegradable and efficient synthesis procedure has the potential for optimization and employed in industrial and domestic wastewater treatment.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

ES: Conceptualization, Formal Analysis, Methodology, Project administration, Writing–original draft, Writing–review and editing. HR: Conceptualization, Data curation, Resources, Supervision, Writing–review and editing. RM: Data curation, Software, Validation, Writing–review and editing. MB: Data curation, Software, Validation, Visualization, Writing–review and editing. TS: Conceptualization, Data curation, Resources, Supervision, Validation, Writing–review and editing. SK: Conceptualization, Methodology, Resources, Supervision, Validation, Visualization, Writing–review and editing. WM: Supervision, Conceptualization, Methodology, Validation, Writing–review and editing.

Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

The authors thank Kimberly Clark Paper Mill for providing the paper sludge and the Vaal University of Technology for offering laboratory space.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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